Transcriptional and Epigenetic Factors Associated with Early Thrombosis of Femoral Artery Involved in Arteriovenous Fistula

Vikrant Rai; Devendra K. Agrawal

doi:10.3390/proteomes10020014

Transcriptional and Epigenetic Factors Associated with Early Thrombosis of Femoral Artery Involved in Arteriovenous Fistula

Rai, Vikrant;Agrawal, Devendra K. 2022-04-30 00:00:00 Article Transcriptional and Epigenetic Factors Associated with Early Thrombosis of Femoral Artery Involved in Arteriovenous Fistula Vikrant Rai and Devendra K. Agrawal * Department of Translational Research, Western University of Health Sciences, Pomona, CA 91766-1854, USA; vrai@westernu.edu * Correspondence: dagrawal@westernu.edu; Tel.: +1-909-469-7040; Fax: +1-909-469-5577 Abstract: Arteriovenous fistulas (AVFs), created for hemodialysis in end-stage renal disease pa- tients, mature through the outward remodeling of the outflow vein. However, early thrombosis and chronic inflammation are detrimental to the process of AVF maturation and precipitate AVF matu- ration failure. For the successful remodeling of the outflow vein, blood flow through the fistula is essential, but early arterial thrombosis attenuates this blood flow, and the vessels become throm- bosed and stenosed, leading to AVF failure. The altered expression of various proteins involved in maintaining vessel patency or thrombosis is regulated by genes of which the expression is regulated by transcription factors and microRNAs. In this study, using thrombosed and stenosed arteries fol- lowing AVF creation, we delineated transcription factors and microRNAs associated with differen- tially expressed genes in bulk RNA sequencing data using upstream and causal network analysis. We observed changes in many transcription factors and microRNAs that are involved in angiogen- esis; vascular smooth muscle cell proliferation, migration, and phenotypic changes; endothelial cell function; hypoxia; oxidative stress; vessel remodeling; immune responses; and inflammation. These Citation: Rai, V.; Agrawal, D.K. factors and microRNAs play a critical role in the underlying molecular mechanisms in AVF matu- Transcriptional and Epigenetic ration. We also observed epigenetic factors involved in gene regulation associated with these mo- Factors Associated with Early lecular mechanisms. The results of this study indicate the importance of investigating the transcrip- Thrombosis of Femoral Artery tional and epigenetic regulation of AVF maturation and maturation failure and targeting factors Involved in Arteriovenous Fistula. precipitating early thrombosis and stenosis. Proteomes 2022, 10, 14. https:// doi.org/10.3390/proteomes10020014 Keywords: arteriovenous fistula; early thrombosis; epigenetic regulation; maturation failure; Academic Editor: Sixue Chen microRNAs; transcription factors; transcriptional regulation Received: 18 March 2022 Accepted: 28 April 2022 Published: 30 April 2022 1. Introduction Publisher’s Note: MDPI stays neu- tral with regard to jurisdictional Arteriovenous fistulas (AVFs) provide vascular access for hemodialysis in patients claims in published maps and institu- with end-stage renal disease [1]. The successful use of an AVF for hemodialysis depends tional affiliations. on the maturation of the fistula through the outward remodeling of the outflow vein to make it appropriate to respond to increased blood flow so that it can be repeatedly can- nulated to provide adequate flow for dialysis [2,3]. The maturation rates of AVFs have been reported to range between 40% and 80%, but these rates decrease with aging, a distal Copyright: © 2022 by the authors. Li- fistula location, and a small vein diameter [4]. Non-maturation of AVF due to early throm- censee MDPI, Basel, Switzerland. bosis, chronic inflammation, and the failure of outward remodeling is an important cause This article is an open access article of AVF failure. Early thrombosis can occur due to hematoma formation, a hypercoagula- distributed under the terms and con- tion state, decreased blood flow rates, intimal injury during AVF creation, and endothelial ditions of the Creative Commons At- cell dysfunction, leading to chronic inflammation and the failure of outward remodeling tribution (CC BY) license (https://cre- [2]. Along with thrombosis, stenosis and neointimal hyperplasia (NIH) of the outflow vein ativecommons.org/licenses/by/4.0/). also contribute to early AVF failure [5,6]. Chronic inflammation involving various medi- ators of inflammation, including triggering receptor expressed on myeloid cells-1 (TREM- Proteomes 2022, 10, 14. https://doi.org/10.3390/proteomes10020014 www.mdpi.com/journal/proteomes Proteomes 2022, 10, 14 2 of 24 1) and toll-like receptor 4 (TLR4), plays a crucial role in NIH, thrombosis, and stenosis [7– 11]. To attenuate vessel stenosis after AVF creation, we targeted chronic inflammation by inhibiting TREM-1 using LR-12 peptides and TLR4 with TAK-242 and performed se- quencing in collected tissues to investigate the changes in gene expression associated with stenosis and thrombosis. Recently, we reported on differentially expressed genes (DEGs) associated with stenosis and thrombosis of the femoral artery involved in AVF [12]. The presence of differentially expressed genes in the thrombosed artery that are in- volved in AVF indicates the change in gene expression associated with AVF creation and failure due to thrombosis. Since the expression of genes is regulated at both transcriptional and post-transcriptional levels [13], we investigated the transcription factors and mi- croRNAs associated with either activated or inhibited networks and regulating the ex- pression of various DEGs expressed in our data set (hereafter referred to as activated or inhibited). Transcription factors (TFs) are regulatory proteins involved in the process of convert- ing DNA to RNA (transcription) that allow the unique expression of a gene in different cell types. The function of TFs is to activate the transcription of DNA but TFs rarely inhibit gene expression [13]. MicroRNAs (miRs) belong to the class of small non-coding RNAs (18- to 25 nucleotide long), which are involved in controlling gene expression post-tran- scriptionally by targeting mRNAs based on sequence complementarity [14,15]. An miRNA binds with its target messenger RNA (mRNA) and blocks its translation or pro- motes its degradation, thereby decreasing gene expression. However, in opposition to the consensus that miRNA reduces gene expression, there is evidence that some miRNAs can upregulate gene expression [16,17]. Furthermore, TFs, including nuclear factor kappa beta (NF-κB), PU.1, the Ets-1 family, activator protein 1 (AP1), Krϋppel-like factor 2 (KLF2), zinc fingers and homeoboxes 2 (Zhx2), and activating transcription factor 4 (ATF4), play a critical role in atherosclerosis [18]. Since TFs and miRNAs regulate gene expression, tar- geting TFs and miRNAs to modulate gene expression favoring attenuated thrombosis, stenosis, and plaque formation will be of therapeutic importance. Decreased thrombosis, stenosis, and excessive NIH will render arteries and veins patent, and this may increase AVF patency. In this study, we have revealed various novel TFs and miRNAs associated with early thrombosis of the femoral artery involved in AVF and regulating various dif- ferentially expressed genes (DEGs) associated with early thrombosis. Thus, targeting these TFs and miRNAs might be of therapeutic and clinical significance. 2. Materials and Methods The material and methods involved in creating AVFs, assessing pre-and post-opera- tive femoral vessels involved in AVFs, tissue harvesting, tissue processing, histology, im- munostaining, and bulk RNA sequencing have been described previously [12]. We choose the swine model because the swine model represents the best choice for studies of occlu- sive arterial and venous diseases and there are similarities in the anatomy, physiology and pathological responses of the human and porcine cardiovascular systems [19]. Addition- ally, this approach enables us to use the same catheters and tools as those used clinically in humans. Female swine were chosen because female swine are easy to handle compared to male swine and because of the fact that, due to their aggressive behavior, nearly 100% of male pigs have been castrated chemically or immunologically, and such procedures change the hormonal pattern and pathophysiological responses that could significantly affect the outcomes and results due to their effect on immune and resident cells. Briefly, following the creation of AVFs in Yucatan miniswine, LR12 (TREM-1 antagonist) + TAK242 (TLR4 antagonist) were locally administered at the site of fistula creation. For controls, a mixture of scrambled peptide +30% ethanol as the vehicle of TAK-242 was used. LR12 was injected once during the surgery and >10 lentiviral particles in 1 mL were injected. TAK-242 was administered at a dose of 3 mg/kg as bolus and then 0.1 mg/kg daily for 7 days and then once weekly for 4 weeks as a maintenance dose. The patency of Proteomes 2022, 10, 14 3 of 24 the femoral artery and vein was assessed with doppler ultrasound, angiography, and op- tical coherence tomography at the end of 12 weeks and the swine were sacrificed for the harvesting of tissues. Femoral arteries from the AVF site and from the contralateral sides were collected. The total RNA, isolated using the TRIzol method from three thrombosed femoral arteries and three contralateral femoral arteries, was sent for bulk RNA sequenc- ing. RNA samples with RIN > 6 were subjected to library preparation and sequencing using the NEBNext Ultra II RNA Library Prep Kit for Illumina and Illumina HiSeq instru- ments at Genewiz LLC (Plainfield, NJ, USA) as mentioned in [12]. IPA was used for the analyses of microRNAs and transcription factors, as detailed in the following sections. 2.1. Ingenuity Pathway Analysis In order to perform the functional analysis to investigate the novel transcription fac- tors and microRNAs, ingenuity pathway analysis (IPA) was conducted by uploading the identified genes from bulk RNA sequencing. The functional IPA analysis and the statisti- cal inference was completed at Bioinformatics and Systems Biology Core at the University of Nebraska Medical Center (UNMC, Omaha, NE, USA). IPA pathway analyses, includ- ing causal network and regulator effect analyses, were performed using IPA (QIAGEN Inc.,Germany, https://digitalinsights.qiagen.com/products-overview/discovery-insights- portfolio/analysis-and-visualization/qiagen-ipa/ (accessed on 31 August 2021)) . Causal network analysis was conducted to examine causal relationships associated with input genes by expanding the upstream analysis to include regulators that are not directly con- nected to targets in the dataset, as well as to identify potential therapeutic or toxicity tar- gets and known drugs and biomarkers. The analysis of regulator effects provides unprec- edented insights into the input data by integrating upstream regulator results with the results of the downstream effects to create causal hypotheses. The results of regulator ef- fects analysis explain the way in which the events occurring upstream cause a particular phenotypic or functional outcome downstream (https://digitalinsights.qiagen.com (ac- cessed on 31 August 2021)). 2.2. Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) Network Analysis Transcription factors regulate the expression of a gene, which in turn regulates pro- tein expression through translation. To delineate the protein–protein interactions regu- lated by the TFs that appeared in our data set, we performed STRING network analysis using https://string-db.org/ (accessed on 19 February 2022). 3. Results IPA analysis revealed microRNAs associated with activated and inhibited networks. The upstream regulator analysis using input genes from the LR12 + TAK242-treated group compared to the contralateral femoral arteries revealed 15 microRNAs (mir-193, mir-365, miR-199a-5p, miR-889-3p, mir-889, mir-1, mir-137, miR-1195, mir-644, miR-293-5p, mir- 122, miR-224-5p, mir-153, mir-346, and mir-467; mir-microRNA, and miR-mature mi- croRNA) but none were associated with activated or inhibited networks. The causal net- work analysis using input genes from the LR12 + TAK242-treated group compared to the contralateral femoral arteries revealed nine microRNAs (miR-889-3p, mir-889, mir-365, miR-199a-5p, mir-15, mir-379, miR-23a-3p, mir-193, miR-96-5p; mir-microRNA, and miR- mature microRNA) but these were not associated with activated or inhibited networks. The upstream regulator analysis using input genes from the LR12 + TAK242-treated group compared to the scrambled peptide + ethanol group arteries revealed 35 microRNAs, and of these, three were inhibited (Table 1) and 32 microRNAs (mir690, miR-1258, miR-690, mir-1258, mir-129, mir-219, mir-363, mir-584, mir-132, miR-150-5p, miR-375-3p, mir-142, mir-204, miR-30c-5p, miR-204-5p, miR-4728, miR-101b-3p, miR-185-3p, miR-4728-3p, mir- 331, mir-663, mir-210, mir-451, mir-33, miR-138-5p, miR-483-3p, mir-155, mir-8, mir-30, Proteomes 2022, 10, 14 4 of 24 mir-17, mir-223, and miR4269; mir-microRNA, and miR-mature microRNA) were not as- sociated with activated or inhibited networks. The causal network analysis, using input genes from the LR12 + TAK242-treated group compared to scrambled peptide + ethanol group arteries, revealed 17 microRNAs and, of these, one microRNA was associated with an activated network and four microRNAs were associated with inhibited networks (Ta- ble 2), whereas 12 microRNAs (mir-124, mir-326, mir-181, mir-132, miR-4269, miR-138-5p, miR-1258, miR-690, mir-1258, mir-219, mir-584, miR-690; mir-microRNA, and miR-mature microRNA) were not associated with activated or inhibited networks. The identified mi- croRNAs were associated with various differentially expressed genes (DEGs) with log2 > 2 or <−2 with p < 0.05 and other DEGs with log2 < 2 or >−2, p < 0.05. From the list of all DEGs, we sorted out the DEGs involved in NIH, plaque formation, endothelial cell (EC) dysfunction, phenotypic changes in VSMCs, proliferation and migration of ECs and VSMCs, angiogenesis, vasculogenesis, immune cell recruitment, and inflammation based on an extensive literature search (the complete list of microRNAs can be found in Supple- mentary File S1). Table 1. MicroRNAs revealed with upstream network analysis: LR12 + TAK242-treated vs. scram- bled peptide and vehicle (30% ethanol)-treated arteries. DEGs with higher expression in scrambled peptide-treated group (log2 > 2, p < 0.05) and with higher expression in LR12 + TAK242-treated group (log2 <−2, p < 0.05). Associated with Inhibited Network DEGs (log2 > 2, p < 0.05) DEGs (log2 <−2, p < 0.05) mir-133 VCAN PPARG, TNFSF10 miR-155-5p BACH1, CD69, CXCL8, PMAIP1, PPL, RIPK1 Table 2. MicroRNAs revealed with causal network analysis: LR12 + TAK242-treated vs. scrambled peptide and vehicle (30% ethanol)-treated arteries. DEGs with higher expression in scrambled pep- tide-treated group (log2 > 2, p < 0.05) and with higher expression in LR12 + TAK242-treated group (log2 < −2, p < 0.05). Associated with DEGs (log2 < 2 Activated DEGs (log2 > 2, p < 0.05) DEGs (log2 < −2, p < 0.05) or >−2, p < 0.05) Network ADAM8,ADAMTS4,ARG1,CD27,CD3D, ADAMTS8,ADIPOR2,AQP11,BCAM, CD5,CSF2RB,CXCL8, DNAH11,DUOX2, CAT,CEBPA,CIDEC,DHH,EIF4EBP1, GP1BA,GP1BB,HPSE,IL1R1,IL2RB,IL7R, miR-205-5p HCAR1,LMO2,MYLPF,PLIN1,PLIN5, CD6, CXCR5, EGR2 KDM6B,LAG3,MMP25,NCOR2,PCDH8, PNPLA2,PPARG,RETSAT,SEMA3G, PHACTR1,PPRC1,PTX3,RELT,SEMA4A, SERPINI1,TEK,WNT11 SERPINB2, STAT4,STEAP4 Associated with inhibited networks ADAM8,ADAMTS4,ALAS2,BCL3,CCR7, CD27,CD3D,CD69, COL6A3,CXCL8, DUOX2, AGT,ALDH9A1,APOD,APOE,ASS1, ECM1,EDIL3,EGR1,FCMR, BCAM,BMX,CAVIN2,CIDEC,CLEC14A,C FERMT3,GP1BA,GPR84, HPSE,ICOS, YP4B1,DDIT4L,ECH1,EIF4EBP1, IL2RB,IL7R, KDM6B,LAG3,MAP3K14, mir-515 ENHO,EPHX2,HCAR1,MMD,MYH4, CD6, CXCR5 MUC13,MYC,NCOR2,PCDH8,PHACTR1, MYL1,OSM,OSMR,PPARG,PLIN5, PLAGL1,PPRC1,PRDM1,PTX3,RCAN1, PNPLA2,PON3,S100A1,SERPINI1, RNF213, SBNO2, SELP, SEMA4A, SERPINB2, TCAP,TEK,TMOD4,TNNC2, TSPAN5 SLAMF7,ST18,STAT4,STC1, TUBB1,VWF ADAMTS1, CD5,CD69,CSF2RB, CXCL8, AGT,ALDH1A1,APOD,APOE,BCAM, DUOX2,EGR3,FCMR,GP1BA,HAS1,ICOS,IL2RB DHH,MYH8,MYL1,MYOT,OSM, mir-153 ,ITGA2B,ITGB7,KDM6B,NCOR2, OSMR,PLIN1,RAMP2,RETSAT, NFATC2,PDE4B,PHACTR1,PRDM1, RNF213, S100A1,SCD,TCAP,TNNC2,WNT11 SERPINB2,ST18,STAT4,STEAP4, TEAD3, VDR ALAS2, BCL3,BLK,CCL22,CSF2RB, ALDH1A1,APOD,APOE,ASS1,BCAM, mir-219 EGR2, IL9R CXCL8,DUOX2,FCMR,ICOS,IL2RB,IL7R, CAT,CAVIN2,LDHB,PPARG,MMD, Proteomes 2022, 10, 14 5 of 24 ITGA2B, NFATC2, PDE4B, PHACTR1, MYL1,MYLPF,MYOT,OSM,PEX11A, PLAGL1, PRDM1,PTX3, RNF213, SBNO2, RAMP1,TEK STEAP4,TUBB1,VWF CCL22,CCR7,DUOX2,HAS1,IL1R1,IL2RB,IL7R,I mir-22 TGA2B,ITGB7, NFATC2,PTX3, ALDH1A2,ASS1,BCAM, MYLPF, TNNC2 EGR2 RCAN1,SERPINB2,STEAP4, VDR IPA analysis revealed transcription factors associated with activated and inhibited networks. The upstream regulator analysis using input genes from the LR12 + TAK242- treated group compared to contralateral femoral arteries revealed 132 TFs (Supplemen- tary File S2 sheet 1). Among 132 TFs, 11 TFs were associated with activated networks, whereas four TFs were associated with inhibited networks (Table 3). The causal network analysis using input genes from the LR12 + TAK242-treated group compared to contrala- teral femoral arteries revealed 91 TFs (Supplementary File S2 sheet 2) and of these, 13 were associated with activated and 14 were associated with inhibited networks (Table 4). The upstream regulator analysis using input genes from the LR12 + TAK242-treated group compared to scrambled peptide + ethanol group arteries revealed 311 TFs (Supplementary File S2 sheet 3) and of them, 14 were associated with activated and 11 TFs were associated with inhibited networks (Table 5). The causal network analysis using input genes from the LR12 + TAK242-treated group compared to scrambled peptide + ethanol group arter- ies revealed 152 TFs (Supplementary File S2 sheet 3) and of these, 22 TFs were associated with activated and 25 TFs were associated with inhibited networks (Table 6). The identi- fied TFs were associated with various differentially expressed genes (DEGs) with log2 > 2 or <−2 with p < 0.05 and other DEGs with log2 < 2 or >−2, p < 0.05. From the list of all DEGs, we sorted out the DEGs involved in NIH, plaque formation, endothelial cell (EC) dysfunc- tion, phenotypic changes in VSMCs, proliferation and migration of ECs and VSMCs, an- giogenesis, vasculogenesis, immune cell recruitment, and inflammation based on an ex- tensive literature search (the complete list of TFs can be found in Supplementary File S2). Table 3. Transcription factors revealed with upstream network analysis: LR12 + TAK242-treated vs. contralateral femoral arteries. DEGs with higher expression in LR12 + TAK242-treated group (log2 > 2, p < 0.05) and with higher expression in contralateral femoral arteries (log2 < −2, p < 0.05). Associated with Activated DEGs (log2 > 2, p < 0.05) DEGs (log2 <−2, p < 0.05) DEGs (log2 < 2 or >−2, p < 0.05) Networks MYOD1 CXCL14,MYH4,MYL1,MYLPF,MYOD1,TNNC2 IGF1 SMARCA4 IL7R,ITGA3, MYL1,MYLPF,TNNC2 CNTN1, MAP1B, MFGE8, IGF1 MEF2C MYLPF,MYOD1,MYOT FRZB, KCNA5, IGF1 MYF6 EGF,IL15,IL7,MYOD1, HOXC8 IL1R2,SLC16A3 SPDEF ITGA3 TNC COL4A2 SPI1 CD1D,CXCL14,IL18,IL1R2,IL7R CD79B IRF1 IL15,IL18,IL7,MMP9 RB1 HOMER1,MYH4,MYH8,MYL1,TCAP,TNNC2 IGF1, COL4A2 ARNT2 APOE,EGF,HOMER1 SIM1 APOE,EGF,HOMER1 Associated with inhibited network HDAC4 HOMER1,MMP9,MYLK2,MYOT CNTN1 KDM5A HOMER1,MYH4,MYH8,MYL1,TCAP,TNNC2 HTT APOE,HOMER1,IL15,MYL1,MYOD1, PRELP COL4A2 VDR IL18,MYH8,S100A8 COL4A2 Proteomes 2022, 10, 14 6 of 24 Table 4. Transcription factors (TFs) revealed with causal network analysis: LR12 + TAK242-treated vs. contralateral femoral arteries. DEGs with higher expression in LR12 + TAK242-treated group (log2 > 2, p < 0.05) and with higher expression in contralateral femoral arteries (log2 < −2, p < 0.05). Associated with DEGs (log2 < 2 DEGs (log2 > 2, p < 0.05) DEGs (log2 < −2, p < 0.05) Activated Networks or >−2, p < 0.05) ANK1,APOE, CASQ1,EGF,HOMER1,IL15,IL18,IL7R, MYF6 AQP5, NOTCH3, TNC IGF1 MYL1,MYLPF,MYOD1,MYOT,TCAP,TNNC2,TRIM63 ANK1,APOE,CASQ1,EGF,HOMER1,IL18,IL7R,MYL1, MYOD1 AQP5, NOTCH3, TNC IGF1 MYLPF,MYOT, TCAP, TNNC2,TRIM63 AMPD1,APOE,CD1D,CD247,CD3G,CYGB,DDIT4L, ADAMTSL4, AEBP1, EGF,FGR, HABP2,ICAM3,IL15,IL1R2,IL7R, MMP9, COL8A1, ELN, FBLN5, NCOA4 IGF1, COL4A2 MMRN1,MYH8,MYLPF,MYOT,MYPN, FRZB, KCNA5, MAP1B, PRKCQ,S100A1, SLN,TFPI,TNNC2,XIRP2 MFGE8, NOTCH3, SLIT3 ANK1, CNKSR1,CXCL14,DOK5,EGF, HOMER1, AEBP1,AQP5,COL8A1, ICAM3,IFIT1,IL15,IL18,IL1R2,IL7,IL7R, MYH4,MYH8, NCOA1 ELN,FBLN5,FMOD, ITGA3, COL4A2 MYL1,MYOT,MYPN,PLIN5,STK17B,TCAP,TNNC2, MAP1B, NOTCH3, SLIT3 TRIM63,XIRP2 CD1D,CD247,CNKSR1,CXCL14,CYGB,DDIT4L, ADAMTSL4, BGN, ELN, FGR, HOMER1,ICAM3,IFIT1,IL15,IL18,IL7, COL8A1,FBLN5,FMOD, IGF1, COL4A2, FOXA1 MMP9,MYH4,MYH8,MYL1,MYLPF,MYOD1,MYPN, FRZB,ITGA3,MAP1B,TNC,N CD79B PLIN5, PRKCQ,S100A1, STK17B,TCAP,TNNC2,XIRP2 OTCH3, PRELP,SLIT3 ITGA3, MAP1B, IGF1,CD79B SMARCC1 CNKSR1,ICAM3,IL7R, MMP9,MYOT NOTCH3,TNC COL4A2 SMARCA4 IFIT1,MYL1,MYLPF,TNNC2 CNTN1 IGF1 HOXC8 IL1R2,SLC16A3 SPDEF ITGA3,TNC COL4A2 IRF9 IFIT1,IL18 Associated with inhibited networks CD1D,CD247,CD3G,CXCL14, HABP2, ICAM3, IFIT1, AEBP1,FBLN5,FMOD, IL15,IL18,IL1R2,IL7R, MYH4,MYH8,MYL1,MYLK2, AHRR FRZB, MFGE8, NOTCH3, COL4A2 MYOT,MYPN,PLIN5,PRKCQ, STK17B,TCAP,TRIM63, SLIT3 XIRP2 ANK1, CXCL14,DOK5, HABP2, IFIT1,IL15,IL18, AQP5,BGN,COL8A1, HDAC3 IL7,IL7R, MYH8,MYLPF, PLIN5, S100A8,SLN, ELN,FBLN5,ITGA3, IGF1, COL4A2 TFPI,TRIM63 KCNA5,NOTCH3, RASL11B CNKSR1,CXCL14,CYGB,DDIT4L,DOK5,EGF,FGR, ADAMTSL4,ADRA1D, HOMER1,ICAM3,IL15,IL18,IL7,MMP9,MYH4,MYH8,MYL1 AEBP1,AQP5,BGN, FBLN5, EID1 IGF1 ,MYOT,MYPN,PLIN5,S100A8, STK17B,TMOD4, FRZB,KCNA5,MAP1B, TNNC2,XIRP2 NOTCH3, SLIT3, TNC APOE,CNKSR1,CXCL14,CYGB,DDIT4L,DOK5,EGF, ADAMTSL4,ADRA1D,AEBP FGR,HOMER1,ICAM3,IL15,IL18,IL7,MMP9,MYH4, 1, FBLN5, FRZB, HOXA10 CD79B MYH8,MYL1,MYOT,MYPN,PLIN5,S100A8, KCNA5,MAP1B, NOTCH3, TK17B,TMOD4, TNNC2,XIRP2 SLIT3, TNC CD1D,COL8A1,CXCL14,DOK5,ICAM3,IFIT1,IL15, MTA2 IL18,IL1R2,IL7R, MMP9,MYH4,MYH8,MYL1,MYLPF, AQP5, ELN,FBLN5, MAP1B, MYOD1,MYOT,PLIN5,S100A8,SLC16A3,TNNC2, TRIM63 ADAMDEC1, C5AR2,CNKSR1,CXCL14,CYGB, ADRA1D,AEBP1, FBLN5, DOK5, FGR, ICAM3,IL18,IL7R, MYH4,MYH8,MYL1, FRZB,ITGA8,KCNA5, HOXA9 CD79B MYOD1,MYOT,MYPN,PLIN5,PRKCQ,S100A8, STK17B, MAP1B, NOTCH3, SLIT3, TNNC2,TRIM63,XIRP2 TNC CD3G,IL7R,MMP9,MYL1,MYLPF,MYOD1, STK17B, TCF3 NOTCH3 IGF1, CD79B TNNC2 CD247,CD3G, IL15,IL1R2,IL7R,MYLK2,MYOT, AHRR FBLN5,NOTCH3 COL4A2 PRKCQ ADRA1D,APOE,CD247,CD3G, IL15,IL1R2,IL7R, AIP FBLN5,NOTCH3 COL4A2 PLIN5,PRKCQ,S100A8 Proteomes 2022, 10, 14 7 of 24 KDM5A HOMER1,MYH4,MYH8,MYL1,TCAP,TNNC2 DNMT3L CASQ1,IFIT1,S100A1,SLIT3,SLN CD79B HOMER1,MMP9,MYH4,MYH8,MYL1, S100A8, EHF NOTCH3 IGF1 TCAP,TFPI,TNC, TNNC2 HDAC4 MYLK2,MYOT SOX15 MMP9, MYOD1 Table 5. Transcription factors (TFs) revealed with upstream network analysis: LR12 + TAK242 treated vs. scrambled peptide and vehicle (30% ethanol)-treated arteries. DEGs with higher expres- sion in LR12 + TAK242-treated group (log2 < −2, p < 0.05) and with higher expression in scrambled peptide-treated group (log2 >2, p < 0.05). Associated with DEGs (log2 < 2 or >−2, p DEGs (log2 <−2, p < 0.05) DEGs (log2 > 2, p < 0.05) Activated Networks < 0.05) ALAS2, ARG1, BCL3, SELP AGT,ALDH1A1,CAT,CEBPA, COL5A1, CXCL8, EGR1,EGR3, ICOS, STAT3 CXCR5, EGR2, IL9R LDHB IL1R1,IL2RB, MYC, SBNO2, NFATC2, PLAGL1,PRDM1 ALDH1A1,ALDH1A2, APOE, ADAM8,ARG1, BCL3, CXCL8, EGR1, ALDH9A1,ASS1,BMX, CAT, EGR3, MYC, DMRT1, TP53 CEBPA, DDIT4L,ECH1, LDHB, ECM1, EDIL3, ITGA2B, ITGB7, NCOR2, EGR2 PPARG, RAMP2, SCD, TCAP, PDE4B, PRDM1, SELP, TMOD4 SERPINB2, VDR AGT,APOE, CEBPA, LDHB, ADAMTS4,BCL3, EGR1, PDE4B, HTT CXCR5, EGR2 MYL1,PPARG COL6A3, PPRC1, PTPN22, RGS14 CD27,CD69, CRTAM,EGR1, IL2RB, MYC, ETS1 HPSE ITGA2B, PRDM1 BCL11B CXCL8 BCL3, CCL22, CCR7,CD69, CXCL8, RELA AGT,APOE, PPARG EGR1, GP1BB, MYC, NFATC2, PDE4B, PRDM1, PTX3,SELP AGT, MYH8,PNPLA2, VDR CXCL8, EGR1, MYC, ITGB7, STAT4,VDR PPARG HDAC4 MYLK2,MYOT SERPINB2 EGR2 RELB CXCL8, MYC, PRDM1, STAT4 MYH4,MYH8,MYL1,TCAP KDM5A TNNC2 ASXL1 PLIN1,PPARG,SCD LEF1 ECM1,MYC, PRDM1 NFKB2 CCR7,CXCL8, MYC CXCR5 NUPR1 MMD ABL2,CXCL8, MYC Associated with inhibited networks BCL3, CCR7,CXCL8, IL1R1, ITGB7, GFI1 CEBPA, EGR2 MYC, STAT4,VDR AGT,APOD,CAT, PPARGC1A LDHB,MYC, PLIN5, PNPLA2, ADAMTS1,IL1R2, COL6A3, OSMR, STC1 SCD,SEMA3G AGT,ASS1,MYH4,MYL1, MYOD1 MYLPF,TNNC2 HLX SEMA3G CXCL8, EGR1, MYC, PRDM1, SPDEF COL5A1, COL6A3,CXCL8 CEBPA, MYH4, MYH8,MYL1, COL5A1, CXCL8, EGR1,EGR3, MYC, RB1 PPARG,RAMP2,TCAP,TNNC2 OSMR, PTX3 IKZF2 CD69,ICOS, IL1R1, LAG3,STAT4 NCOA1 CEBPA, PPARG EGR1, MYC NKX2-3 CAVIN2 CXCL8, RNF213 Proteomes 2022, 10, 14 8 of 24 NEUROG1 ASS1,CAVIN2 KMT2D IGSF1,PPARG Table 6. Transcription factors revealed with causal network analysis: LR12 + TAK242-treated vs. scrambled peptide and vehicle (30% ethanol)-treated arteries. DEGs with higher expression in LR12 + TAK242-treated group (log2 > 2, p < 0.05) and with higher expression in scrambled peptide-treated group (log2 <−2, p < 0.05). Associated with DEGs (log2 < 2 Activated DEGs (log2 >2, p < 0.05) DEGs (log2 <−2, p < 0.05) or >−2, p < 0.05) Networks ADIPOR2, AGT, AQP11, ASS1,BACH1, ABL2,ALAS2,ARG1,BLK,CD27,CD3D, BCAM, BST1,CAVIN2, CEBPA, DDIT4L,ECH1, CHST2,COL5A1,CXCL8,EGR3, DMRT1, EPHX2, LDHB, MDK, MYL1,MYLPF,MYOT, ECM1, FCMR,GP1BA,GP1BB,GPR84, MEF2C PEX11A, PLIN5, PNPLA2, IL1R2,IL2RB,IL7R,ITGA2B,ITGB7,LAG3,LMO2 EGR2, IL9R RAMP1,RAMP2,RETSAT, SCD, SLC9A3R2, ,NFATC2,PLIN1,PLAGL1, PPRC1, TEK, TMOD4, PRDM1,PTPN22,RNF213, SEMA4A,SELP, TNNC2,TSPAN5 ST18,STEAP4, TUBB1,VDR,VWF ADAMTS1,ALAS2, BCL3, CD27, AGT,ALDH1A1,ALDH1A2,AQP11, COL5A1,COL6A3,CXCL8,DMRT1, BCAM,CAT,CAVIN2,CEBPA,CIDEC, EGR1,EGR3,GPR84,HAS1,HPSE,ICOS, DHH, EIF4EBP1,EPHX2, KLHL31, IL1R2,IL1RL1,KDM6B,LAG3,LMO2, CXCR5, EGR2, ISL1 LDHB,LGALS12, MMD,MYH4,MYL1, MYOT, MYC,MMP25,NFATC2,OSMR,PCDH8,PRDM1 IL9R PEX11A, PNPLA2, RAMP1, , PLAGL1,PLIN1,SBNO2, RAMP2,RETSAT,SERPINI1,SFRP5, TCAP, SEMA4A,SERPINB2,ST18,STEAP4, TEK,TNNC2,TSPAN5, WNT11 TCF7,TEAD3,TUBB1 COL5A1,COL6A3,EGR1,FCMR,GP1BA, ALDH1A1,APOE,ASS1,CAT,CAVIN2, HAS1,IL1R1,IL1RL1,IL2RB,ITGA2B, CIDEC,CYP4B1,EPHX2, HCAR1, EHMT2 ITGB7,MUC13,NFATC2,PDE4B, EGR2 LDHB,MMD,MYL1,MYLPF,PEX11A, PRDM1,PLIN1,PTX3,SERPINB2, PLIN5,PPARG, SCD,TCAP TUBB1,VDR,VWF ABL2,ARG1,ADAM8,ADAMTS1, ADAMTS4,BCL3, CCL22,CCR7, CD3D,CD5,CD27,COL6A3, CXCL8, ADRB1,ALDH1A1,ALDH1A2, DUOX2,EGR3, ECM1, EDIL3, GP1BA,GPR84, ALDH9A1,APOD,APOE,BCAM, BMX, HAS1, HPSE, IL1RL1,IL2RB, ITGB7, CAT,CAVIN2,CEBPA,CIDEC,CYP4B1, ITGA11,KDM6B, ASXL1 DDIT4L,ECH1,EPHX2,HCAR1,MYLPF, CXCR5, IL9R LAG3,LMO2,MMP25,MUC13,NAV1, NFATC2, PEX11A,PLIN5,PNPLA2,PPARG, NCOR2, OSM,OSMR, RAMP2,RETSAT,SCD, SEMA3G, PHACTR1,PLAGL1,PLIN1,PRDM1, SERPINI1, TCAP,TMOD4,TNNC2, WNT11 RCAN1,RNF213,SBNO2,SELP, SLAMF7,ST18,STAT4,STC1,STEAP4, TUBB1,VDR ADAM8,ADAMTS4,ALAS2,BCL3, CCL22,CCR7,CD3D,CD27,CD69, AGT, ALDH1A2,ALDH9A1, APOD, COL6A3,CXCL8, DUOX2,ECM1,EGR1, APOE,ASS1,BMX,CAVIN2,CIDEC, FERMT3,GP1BA,GPR84,MYC,EDIL3, CLEC14A,CYP4B1,DDIT4L,ECH1, ICOS,IL1R2,IL2RB,ITGB7,IL7R,KDM6B, CD6, CXCR5, BCL11B EIF4EBP1,ENHO,EPHX2,HCAR1,HPSE,LDHB, LAG3,MUC13,NCOR2,OSM,OSMR, EGR2 MMD,MYLPF,PEX11A,PPARG, PHACTR1,PCDH8,PLAGL1,PLIN5,PRDM1, RAMP1,SERPINI1,TCAP,TEK,TMOD4,TSPAN PPRC1, PTX3, RCAN1, RNF213, 5,WNT11 SBNO2, SEMA4A,SERPINB2,SLAMF7, ST18,STAT4, TCF7,TUBB1,VWF ADAM8, ADAMTS4,ALAS2, BCL3, AGT,ALDH1A2,ALDH9A1,APOD, CCL22,CCR7, CD3D,CD27, CD69,COL6A3, CD6, CXCR5, ANKRD42 APOE,ASS1,BMX,CAVIN2,CIDEC, CXCL8,DUOX2,ECM1, EGR2 CLEC14A,CYP4B1,DDIT4L,ECH1, EGR1, FERMT3,ICOS, EDIL3, GP1BA, Proteomes 2022, 10, 14 9 of 24 EIF4EBP1,ENHO,EPHX2,HCAR1,HPSE,LDHB, GPR84,IL1R2,IL2RB, ITGB7, IL7R, MMD,MYLPF,PEX11A,PPARG, KDM6B,LAG3, MUC13, MYC,NCOR2, RAMP1,SERPINI1,TCAP,TEK,TMOD4, OSM,OSMR, PCDH8, TSPAN5,WNT11 PHACTR1,PLAGL1,PLIN5,PPRC1,PRDM1, PTX3, RCAN1,RNF213,SBNO2, SEMA4A,SERPINB2, SLAMF7,ST18,STAT4, TCF7,TUBB1,VWF ADAM8,ADAMTS4,ALAS2, BCL3, CCL22,CCR7, CD3D, CD27, AGT,ALDH1A2,ALDH9A1,APOD, CD69,COL6A3,CXCL8, DUOX2, APOE,ASS1, BMX,CAVIN2, CIDEC, EGR1, FERMT3,GP1BA,GPR84,ICOS, MYC, CLEC14A, CYP4B1,DDIT4L,ECH1, ECM1, EDIL3, IL1R2,IL2RB, ITGB7, IL7R, CD6, CXCR5, YBX1 EIF4EBP1,ENHO,EPHX2, HCAR1, KDM6B, EGR2 HPSE,LDHB,MMD, MYLPF,PEX11A, LAG3, MUC13, NCOR2, OSM,OSMR, PCDH8, PPARG,RAMP1,SERPINI1,TCAP, PHACTR1,PRDM1, PPRC1, PLAGL1,PLIN5, TEK,TMOD4,TSPAN5,WNT11 PTX3, RCAN1, RNF213,SBNO2, SEMA4A,SERPINB2, SLAMF7,ST18,STAT4, TCF7,TUBB1,VWF ADAMTS4,ALAS2, BCL3, BLK, CCL22,CCR7, CD69, CSF2RB,CXCL8, DMRT1, FERMT3,GP1BA, HAS1, AGT,APOE,ASS1,CAT,CEBPA, ICOS, IL1R1,IL1R2,IL1RL1, IL7R, GATA4 CLEC14A,EIF4EBP1,MYL1,MYLPF, CXCR5 ITGA2B,KDM6B,LMO2, MYC, MYOT,PPARG, RAMP1, TCAP NFATC2,OSM,OSMR,PDE4B, PLAGL1,PRDM1, PTX3, RCAN1,SELP, SERPINB2, ST18, TUBB1,VDR ALAS2, ARG1, CCL22,CCR7,CD5, AGT,APOE,BCAM,CAT,CIDEC, CD27,COL5A1,EGR3, GP1BA,GP1BB, CYP4B1,DHH, HCAR1,MMD,MYH4, HAS1,ICOS, IL2RB, ITGA2B, IL7R, CCND1 MYH8,MYL1,MYLPF,MYOT, PEX11A, LMO2,MYC, MAP3K14,MUC13, NCOR2, CD6, IL9R PLIN5,PNPLA2,PPARG,RAMP2,RBP7, SCD, NFATC2,PCDH8,PLAGL1, TCAP,TNNC2,TSPAN5, WNT11 PLIN1,PPRC1, PTX3,SBNO2,STEAP4, TEAD3,TUBB1,VDR ALAS2, ARG1, BCL3,CCL22,CCR7, CD69,CXCL8, EGR1, FCMR,GP1BA, AGT,APOE, BCAM, CAVIN2,MMD, HPSE,ICOS, IL1R1,IL1RL1,IL2RB, IL7R, MYB EGR2 RCAN1 KDM6B, LMO2,MYC, ITGA2B, PDE4B, PRDM1, PTPN22, PTX3,SERPINB2, ST18,STEAP4,TUBB1,VDR,VWF BCL3, CCL22,CCR7, CD69,CXCL8, EGR1, AGT,ALDH1A1,ALDH1A2,APOE, HAS1,ICOS, IL7R, KDM6B,MYC, ITGA2B, LRRFIP1 EGR2 CEBPA,PPARG, WNT11 PDE4B, PLIN1,PRDM1, PTX3,SERPINB2, ST18,STAT4, TCF7 AGT,ALDH1A1,ALDH1A2,APOD,CAT,CEBP ADAM8,ADAMTS1,CCL22,CCR7, CXCL8, A, EGFL7,LDHB, MYH8,PLIN5, COL6A3, EGR3,IL1R1,IL1R2, IL2RB, MYC, ESRRA PNPLA2,PPARG, SEMA3G,STC1, OSMR, PLIN1,PDE4B, PRDM1, PTX3, STEAP4, WNT11 TCF7 ALAS2, BCL3, CCL22,CCR7,CD27, CD69, AGT,CEBPA,LDHB, MYL1,PPARG, SCD, CXCL8, EGR1, ICOS, IL7R, IL2RB, KDM6B, NOTCH4 TCAP,TEK LMO2, OSM, PRDM1, PTX3,SERPINB2, STAT4, ST18,VWF ALAS2, BCL3, CCL22, CXCL8,CD69, AGT, ALDH1A1, CAT,CAVIN2, DUOX2,FCMR,GP1BA,GPR84, IL7R, GATA1 IL9R LDHB, MMD, PPARG ICOS, IL1R1,IL1R2,IL1RL1, ITGA2B, LMO2, MYC, NFATC2, PHACTR1, PLAGL1,PDE4B, Proteomes 2022, 10, 14 10 of 24 PTPN22,RNF213, SBNO2, STAT4, ST18,TUBB1,VDR,VWF BCL3, BLK,CCL22,CCR7,IL7R,KDM6B, AGT,APOE,ASS1, CD69,CXCL8, DMRT1,EGR1,ICOS, MYC, TBX5 EGR2 MYL1,MYLPF,MYOT, PPARG NFATC2, PLAGL1,PTX3, SERPINB2, ST18,STAT4 AGT,APOD, BCAM,CAT, CYP4B1, ADAMTS1,ARG1,COL6A3, IL7R,CXCL8, NRIP1 LDHB,PLIN5,PNPLA2,PPARG, EGR1, IL1R1,IL2RB, OSMR, PLIN1, PTX3, EGR2 SCD,SEMA3G,STC1 STEAP4 ARG1, BCL3, CCL22,CCR7,CD69,EGR1, TRIM32 AGT,APOE, CEBPA,PPARG ICOS, IL7R,KDM6B,PRDM1, PTX3, SELP, CXCR5 SERPINB2, STAT4, ST18 BCL11B CXCL8, IL7R HDAC4 MYLK2,MYOT EGR2 Associated with inhibited networks AGT,ALDH1A1,ALDH1A2,APOE, ALAS2, ARG1, BLK, CD3D,CD27, ASS1,CASQ1,CAT, CEBPA,CIDEC, CHST2,COL5A1, CSF2RB, DUOX2, CYP4B1,DDIT4L,ECH1, EIF4EBP1, EGR3, FCMR,GP1BA,GP1BB,IL1R2, IL1RL1, EPHX2,HCAR1,LDHB, MDK,MMD, DMRT1, ITGA2B, KDM6B, LMO2, MUC13, HDAC5 CD6, EGR2, IL9R MYC,MYH4,MYH8,MYL1,MYLPF, NFATC2,OSM,OSMR, PHACTR1, MYOT,PEX11A, PLIN5,PNPLA2, PLAGL1,PLIN1,PTPN22, PPARG, RAMP2, RETSAT,SCD, SERPINI1, RCAN1,RNF213,SBNO2,SELP, TEK,TNNC2 STC1,ST18,STAT4,TUBB1,VDR ABL2,ADAMTS1,ADAM8,ADAMTS4,ARG1,B ADRB1,ALDH1A1,ALDH1A2, CL3,CCL22,CCR7,CD3D,CD5, ALDH9A1,APOD,APOE,BCAM, CD27,COL6A3, DUOX2,EGR3, ECM1, EDIL3, BMX,CAT,CAVIN2,CEBPA,CIDEC, GP1BA,GPR84, HAS1, HPSE, IL1R2,IL1RL1, CYP4B1,DDIT4L,ECH1, EPHX2, ITGA11,ITGB7,KDM6B, LAG3,LMO2, SREBF1 CXCR5, IL9R HCAR1,MDK,MYL1,MYLPF,PEX11A, MMP25,MUC13,MYC, PLIN5,PNPLA2,PPARG, RAMP2, NAV1,NFATC2,OSM,OSMR,PHACTR1,NCOR RETSAT,SCD, SEMA3G,SERPINI1, 2, PLAGL1,PLIN1,PRDM1, TCAP,TMOD4,TNNC2,WNT11 RCAN1,RNF213,SBNO2,SELP,SLAMF7, STC1,ST18,STAT4, TUBB1,VDR ABL2,ADAM8,ADAMTS4, ADIPOR2, ARG1, BCL3,CCL22,CCR7,CD3D, CD5, ADRB1,AGT,ALDH1A2,ALDH9A1, CD27,CXCL8,DUOX2,EGR1,EGR3, HAS1, APOD,APOE,BMX,CAT,CAVIN2, HPSE, IL1RL1, ECM1, EDIL3, GP1BA,GPR84, CEBPA,CIDEC, CYP4B1,DDIT4L,DHH, ITGB7, ITGA11, ECH1, EPHX2,HCAR1,MYH4,MYH8, MED24 KDM6B,LAG3,LMO2,MAP3K14, MUC13, CXCR5, IL9R MYLPF,MYOT,PEX11A,PLIN5, MYC, MMP25,NCOR2, NFATC2, PNPLA2,PPARG, RAMP2,RETSAT, NAV1,OSM,PHACTR1, SCD,SEMA3G, SERPINI1,SGK2, PLAGL1,PLIN1,PRDM1,RCAN1,RNF213,SBN STC1,TCAP, TMOD4,TNNC2,WNT11 O2, SEMA4A,SELP, ST18,STAT4, SLAMF7,TEAD3, TUBB1,VDR ABL2, ADAMTS1,ADAMTS4,ADAM8, ARG1, BCL3, CCL22,CCR7, CD3D,CD5, ADRB1,ALDH1A1,ALDH1A2, CD27,COL6A3,CXCL8, DUOX2,EGR3, ALDH9A1,APOD,APOE,BCAM, BMX, EGR1,GP1BA,GPR84,IL1RL1,IL2RB, CAT,CAVIN2,CEBPA,CIDEC,CYP4B1,DDIT4L ECM1,EDIL3,HAS1, HPSE,ITGB7, Ncoa6 ,ECH1,EPHX2,HCAR1,MYLPF, CXCR5, IL9R ITGA11,KDM6B,LAG3,LMO2,MMP25,MUC13, PEX11A,PLIN5,PNPLA2, RAMP2, NAV1,NCOR2,NFATC2, OSM, RETSAT, SCD, SEMA3G,SERPINI1, OSMR,PHACTR1,PLAGL1,PLIN1, PRDM1, TCAP,TMOD4,TNNC2, WNT11 RCAN1,RNF213,SBNO2,SELP, SLAMF7, STC1,ST18, STEAP4,STAT4, TUBB1, VDR Proteomes 2022, 10, 14 11 of 24 ADAM8,ADAMTS4,ALAS2, BCL3, CCL22,CCR7, CD3D,CD27,CD69, COL6A3, AGT, ALDH1A2,ALDH9A1,APOD, CSF2RB,CXCL8, DUOX2, ECM1, EDIL3, APOE,ASS1, BMX,CAVIN2,CIDEC, FERMT3,GP1BA,GPR84, HPSE,ITGB7,ICOS, CLEC14A,CYP4B1,DDIT4L, ECH1, IL1R2,IL2RB, IL7R, CD6, CXCR5, ZBTB32 EIF4EBP1,ENHO,EPHX2,HCAR1, KDM6B,LAG3,MYC, MUC13,NCOR2, EGR2 LDHB,MMD,MYLPF, PEX11A,PPARG, OSM,OSMR,PHACTR1,PCDH8,PLAGL1,PLIN RAMP1,SERPINI1,TCAP,TEK,TMOD4,TSPAN 5,PPRC1,PRDM1, PTX3,RCAN1, 5,WNT11 RNF213,SBNO2, SEMA4A, SERPINB2, SLAMF7,ST18,STAT4, TCF7,TUBB1, VWF ADAM8,ALAS2, CD3D,COL5A1, AGT, ALDH1A2,APOE,ASS1,BACH1, COL6A3, CSF2RB,EGR3, FCMR, HAS1, ICOS, CAT,CAVIN2, CEBPA,DHH, EPHX2, IL1R2,IL1RL1,IL2RB, IL7R,LAG3, MYC, NKX2-1 LDHB,LGALS12,MDK,MMD,MYH4, ITGA2B, MMP25, NAV1,NCOR2, NFATC2, CD6, CXCR5, IL9R MYH8,MYL1,MYLPF,PEX11A, RAMP1, OSM,OSMR, PLAGL1,PLIN1, PRDM1, PTX3, SCD, TCAP,WNT11 RCAN1,SBNO2,SELP, SERPINB2, TCF7,TUBB1, VDR,VWF ADRB1,AGT,ALDH1A1,ALDH1A2, ARG1, BCL3, CCL22,CCR7,COL5A1, ALDH9A1,APOD,ASS1,BACH1,BCAM, EGR1,EGR3, HAS1, HPSE,IL1R1,IL2RB, ECM1, BMX,CAVIN2,CIDEC,CYP4B1,DDIT4L,DHH,E EDIL3, FCMR,GP1BA,ITGB7, CXCR5, EGR2, NEUROG2 CH1,EIF4EBP1,EPHX2, HCAR1, KDM6B,MMP25,MUC13,NFATC2,OSM,OSMR IL9R LDHB,MDK,MMD,MYH4,MYL1, , PLAGL1,PLIN1,RCAN1, MYLPF,PLIN5,RAMP2,TCAP,TEK, SBNO2,ST18,STAT4,STEAP4, TCF7, TMOD4, WNT11 TUBB1,VWF ADAM8,ARG1, BCL3, CD69, CD3D, CSF2RB,CXCL8,DUOX2, EGR1,EGR3, FCMR, GPR84, HSH2D,IL1R2,IL1RL1, ALDH1A2,APOE,AQP11, ASS1, BACH1, BST1, IL2RB, ITGB7, KDM6B,LAG3,MYC, DLX2 CEBPA,EIF4EBP1, EGR2 NFATC2,OSM, MMP25,NAV1, ENHO,FFAR4,MMD,TSPAN5, WNT11 PHACTR1,PRDM1,PTX3,RNF213,SBNO2,SELP . SEMA4A, SLAMF7, SERPINB2, ST18,STAT4, TCF7,VDR ADAMTS1, ADAMTS4, ADIPOR2, CD27, ADRB1,AGT,APOD,APOE,AQP11,ASS1,CAVI CD69, CD3D,CD5,COL5A1, N2,CEBPA,DNAH11, CSF2RB,COL6A3,CXCL8, EGR1, EIF4EBP1,ENHO,EPHX2,HCAR1,MMD,MYH4 FCMR,GP1BA, HAS1,ICOS,IL1R2, IL1RL1, NCOA6 IL9R ,MYLPF, PEX11A,PON3, PPARG,RETSAT, ITGA2B, KDM6B, LMO2, MAP3K14,MYC, S100A1, SEMA3B, OSM,PHACTR1, PCDH8,PLAGL1,PLIN1, SERPINI1,SLC9A3R2,TNNC2, WNT11 PTX3, RELT,RNF213, SBNO2,SEMA4A, STAT4, STC1,TCF7,TUBB1, VWF,VDR ADAM8,ARG1, BCL3, CD3D,CD69, CSF2RB,CXCL8, DUOX2,EGR1,EGR3, FCMR, ALDH1A2,APOE,AQP11, ASS1, GPR84, HSH2D,IL1R2,IL1RL1, FOXJ1 BACH1,BST1,CEBPA,EIF4EBP1,ENHO, IL2RB, ITGB7, KDM6B,LAG3, MMP25, EGR2 FFAR4,MMD,TSPAN5, WNT11 NAV1,NFATC2,OSM,PHACTR1,PRDM1, PTX3,RNF213,SBNO2, SELP, SEMA4A, SLAMF7,SERPINB2, ST18, STAT4, TCF7,VDR ADAM8,ARG1, BCL3, CD3D,CD69, CSF2RB,CXCL8,DUOX2, EGR1,EGR3, FCMR, GPR84,HSH2D,ITGB7,IL1R2, IL1RL1,IL2RB, ALDH1A2,APOE,AQP11,ASS1,BACH1, KDM6B,LAG3,MYC, MMP25,NAV1, PRDM1, FOXD1 BST1,CEBPA,ENHO,FFAR4,MMD, EGR2 PTX3, TSPAN5, WNT11 NFATC2,OSM,PHACTR1,RNF213,SBNO2, SELP, SERPINB2, SEMA4A, SLAMF7,ST18,STAT4, TCF7,VDR Proteomes 2022, 10, 14 12 of 24 ADRB1,AGT, ALDH1A1,ALDH9A1, ALAS2, BCL3, COL5A1, CSF2RB, CXCL8, APOD,ASS1,BACH1,BCAM, BMX, DUOX2,EGR3,FCMR,GP1BA, CIDEC, CYP4B1,DDIT4L,DHH, ECH1, IL1R1,IL1RL1,IL2RB, ECM1, EDIL3, ITGB7, ARHGAP35 EIF4EBP1, HCAR1,KDM6B,LDHB, CXCR5, IL9R MUC13,NFATC2,OSM,OSMR, MMD,MYH4,MYLPF,PEX11A, PLIN5, PLAGL1,PLIN1, PTX3,RCAN1,SBNO2, RAMP1,RAMP2, SCD,TCAP,TEK, STEAP4, ST18,STAT4, TUBB1,VWF TMOD4,TNNC2 ADRB1,ALDH1A2,APOE,CAT,CAVIN2,CEBP ARG1, CCL22,CCR7, CD69,COL5A1, A,CIDEC,CYP4B1, HCAR1, MED1 COL6A3,EGR1,IL1R2,IL1RL1,IL2RB, MYC, MUC13, PEX11A,PLIN5,PNPLA2, PPARG, PDE4B, PLIN1,PRDM1, STC1,VDR SCD,SEMA3B ALDH1A2,APOE, CAT,CAVIN2, ARG1, CCL22,CCR7,CXCL8, Ncoa6 CEBPA,CIDEC, CYP4B1,HCAR1, PLIN1,PRDM1, VDR MUC13,PEX11A, PLIN5, SCD ALDH1A2,APOE, CAT,CAVIN2, ARG1, CCL22, CEBPA,CIDEC, CYP4B1,HCAR1, ZNF423 CCR7,CXCL8, MYC, MUC13, PEX11A, PLIN5,PPARG, PLIN1,PRDM1, VDR SCD ALAS2, ARG1, BCL3, CCL22,CCR7, AGT, ALDH1A2,APOE, CAT,CAVIN2, CD69,CXCL8, EGR1,EGR3,ICOS, IL7R, CITED2 CIDEC,CYP4B1, HCAR1,PEX11A, KDM6B,MUC13, MYC, PLIN1,PTX3, PLIN5,PPARG, SCD,SEMA3B,TEK SERPINB2, ST18,STAT4,VDR ALAS2, BCL3, CD69, CXCL8, CCL22, DUOX2, FCMR,GP1BA,GPR84, IL7R, ICOS, IL1R1,IL1R2,IL1RL1, ITGA2B, AGT, ALDH1A1, CAT,CAVIN2, CBFA2T3 LMO2,MYC,NFATC2,PHACTR1, IL9R LDHB, MMD,PPARG PLAGL1,PDE4B, PTPN22,RCAN1, RNF213,SBNO2, STAT4,ST18,TUBB1, VDR, VWF CD3D,CD5, CXCL8, ICOS,IL1RL1, IL2RB, AGT,ASS1, EIF4EBP1,MYL1,MYLPF, TCF12 ITGB7, IL7R,LMO2, IL9R PPARG,TNNC2 PRDM1, SEMA4A, SLAMF7 APOE, ASS1,CAT,CEBPA,CIDEC, ECH1, ARG1, LMO2,MYC, PLIN1, HLF MMD, PEX11A,PLIN5, PNPLA2, PPARG,RETSAT,SCD IKZF2 CD69,ICOS, IL1R1, LAG3,STAT4 MYOD1 AGT,ASS1,MYL1,MYLPF,TNNC2 GFI1 CEBPA BCL3, CCR7,IL1R1, IL7R,ITGB7, STAT4 BCL3, CXCL8, EGR1, KDM6B, HLX RAMP1,RAMP2,SEMA3G,TSPAN5 MYC, PRDM1 SPDEF COL5A1, COL6A3,CXCL8 STRING network analysis showed the interaction of various proteins with each other. STRING network analysis for genes (encoded by genes being regulated by TFs in this study) showed protein–protein interactions among each other. Specifically, ARNT2 showed interaction with HIF-1α; BCL11B with SIRT2, HDAC1, and HDAC2; CITED2 with HIF-1α, ARNT, FOXO1, and CREBBP; ETS1 with FOXO1, JUN, MAPK1, and FOS; MED1 with PPARG and ESR1; MYOD1 with HDAC4, PPARGC1A, HDAC5, MEF2C, and GATA4; NFKB2 with NFKB1, RELA, and RELB; SPI1 with GATA1, CEBPA, and JUN; and STAT3 with JAK1, JAK2, HIF-1α, IL10RA, and HSP90AA1 (Figure 1). Similarly, CCL5 in- teracted with CCR2, CCR3, CCR5, CCR1, IL6, IL10, IL1B, and TNF; RSRRA with PPARGC1A, HIF-1α, and MEF2C; GFI1 with SPI1; HDAC3 with RELA, PPARG, and HDAC4; HDAC5 with MEF2A; NEUROG2 with ISL1; and VDR with MED1 (Figure 2). Proteomes 2022, 10, 14 13 of 24 The analysis showed protein–protein interaction by the proteins involved in vascular pa- thologies, immune cell responses, inflammation, and hypoxia and this suggests their probable role in AVF maturation and maturation failure. ARHGAP3 ARNT MED1 STAT BCL11B ISL1 CCND ETS1 SPI1 Proteomes 2022, 10, 14 14 of 24 MEF2C CITED NFKB Figure 1. STRING network analysis for protein–protein interactions for Rho GTPase-activating pro- tein 35 (ARHGAP35), aryl hydrocarbon receptor nuclear translocator 2 (ARNT2), mediator of RNA polymerase II transcription subunit 1 (MED1), signal transducer and activator of transcription 3 (STAT3), B-cell lymphoma/leukemia 11B (BCL11B), insulin gene enhancer protein ISL-1 (ISL1), G1/S-specific cyclin-D1 (CCND1), transcription factor PU.1 (SPI1), myocyte-specific enhancer factor 2C (MEF2C), Cbp/p300-interacting transactivator 2 (CITED2), and nuclear factor NF-kappa-B p100 subunit (NFKB2). HDAC PPARGC1A GFI1 ESRRA HDAC MYB Proteomes 2022, 10, 14 15 of 24 MYOD1 NEUROG SMARCC1 LRRFIP CCL5 VDR Figure 2. STRING network analysis of protein–protein interaction for peroxisome proliferator-acti- vated receptor gamma coactivator 1-alpha (PPARGC1A); histone deacetylase 3 (HDAC3); zinc fin- ger protein Gfi-1 (GFI1); steroid hormone receptor ERR1 (ESRRA); histone deacetylase 5 (HDAC5); transcriptional activator Myb (MYB); myoblast determination protein 1 (MYOD1); neurogenin-2 (NEUROG2); Swi/snf-related, matrix-associated, actin-dependent regulator of chromatin subfamily c member 1 (SMARCC1); leucine-rich repeat flightless-interacting protein 1 (LRRFIP1); C-C motif chemokine ligand 5 (CCL5); and vitamin D receptor (VDR). 4. Discussion Intimal injury during AVF creation induces NIH, which helps in vascular remodeling and makes vessel suitable for increased blood flow through the outflow vein. However, excessive NIH and early thrombosis of the inflow artery or excessive NIH in the outflow vein can lead to vessel stenosis and AVF maturation failure [12]. Neointimal reendotheli- alization, with the aim of obtaining an intact endothelium, is necessary for the proper healing and AVF maturation and involves endothelial cell (EC) proliferation and migra- tion and the formation of tight adherence junctions. Small GTPases regulate these pro- cesses, and their dysregulation may cause vascular pathologies, including atherosclerosis (plaque formation) and angiogenesis. Rho GTPases (Rho, Rac, and Cdc42) play a critical role in vascular pathologies and become activated when they bind to GTP and are inacti- vated when they bind to GDP. RhoGTPase also plays a role in regulating molecular pro- cesses such as contraction, migration, proliferation, and the differentiation of smooth mus- cle cells and fibroblasts [20–22]. This suggests the critical role of RhoGTPase in vessel re- modeling, involving the migration and proliferation of ECs and VSMCs. These molecular processes are regulated by various genes and increased or decreased gene expression may alter the migration and proliferation of ECs and VSMCs. Gene expression is regulated by Proteomes 2022, 10, 14 16 of 24 TFs [13] and the inhibition of transcription factor Rho GTPase-activating protein 35 (ARHGAP35), as demonstrated in this study by thrombosed artery RNA seq data, in as- sociation with various DEGs involved in VSMC and EC proliferation and migration, an- giogenesis, and inflammation (Table 6), suggesting the role of ARHGAP35 in early throm- bosis and thereby in AVF maturation failure. However, the underlying mechanistic as- pects warrant further investigation. Hypoxia is another critical factor regulating NIH, EC, and VSMC migration and pro- liferation, extracellular matrix (ECM) remodeling, and vessel remodeling [23]. Hypoxia is caused due to disruption of the vasa vasorum during the creation of AVF and this induces transcription factors including hypoxia-inducible factors (HIFs) such as HIF-1α, HIF-2α, HIF-3α, HIF-1β (aryl hydrocarbon receptor nuclear translocator; ARNT), and HIF-2β (ARNT2) [23]. In this study, we observed activated ARNT2 in the thrombosed arteries (Table 3) with ApoE, a gene regulating vasodilatation and anti-remodeling, as a target DEG in the data set. This suggests that ARNT2 plays a critical role in AVF non-maturation. Aryl hydrocarbon receptors also play a critical role in ischemia-induced angiogenesis [24] and angiogenesis plays a critical role in plaque vulnerability [25], thrombosis [26], and short expectancy of AVF [27]. Activated ARNT2 in this study might be a therapeutic target to facilitate AVF maturation, as aryl hydrocarbons have been suggested as a therapeutic target in cardiovascular diseases [28,29]. Excessive intimal hyperplasia is also a result of increased ROS production and inflammation, mediated by macrophages that are deficient in MED1 [30]. Inhibited MED1 (Table 6) and the infiltration of pro-inflammatory immune cells, including macrophages, in the thrombosed and intimal hyperplasia region of femo- ral arteries in this study support the notion of the presence of inflammation [12] and a the possible role of MED1-deficient macrophages in early thrombosis and AVF failure. In- flammation within the evolving plaque is associated with the activation of signal trans- ducers and activators of transcription (STAT)1 and STAT3, and STATs function as intra- cellular regulators of vascular remodeling [31]. STAT3 is also involved in collagen-in- duced platelet aggregation and thrombosis [32] and was found to be activated in this study, indicating its role in early arterial thrombosis. BCL11B (B-cell leukemia 11b) is another transcription factor that was found to be elevated (Table 5) in the upstream network analysis of our data. BCL11B plays a crucial role in the development, proliferation, differentiation, and survival of T cells. Valisno et al. reported the expression of BCL11B in VSMCs and its role in aortic smooth muscle func- tion and vascular stiffness [33]. Since vascular stiffness adversely affects AVF maturation, increased expression of BCL11B in thrombosed arteries suggests its pathological role and its therapeutic potential as a target. Vascular stiffness, mainly arterial stiffness, plays a critical role in hypertension, involving a change in the gene expression and phenotype of VSMCs and ECs and their focal contacts. In ECs and VSMCs, Lin11-Isl1-Mec3 (LIM) do- main-containing proteins play a critical role as mechanotransducers [34] and hemody- namic changes occur after AVF creation and contribute to AVF maturation and/or failure. Investigating the role of Isl1, which was found to be activated in this study, will be of significance. Differentiation and proliferation of VSMCs and dysfunction of ECs play a crucial role in stenosis, thrombosis, and remodeling and determine the fate of AVFs. c- Myb, a TF that was found to be activated in this study, regulates VSMC progenitor cells, VSMC differentiation, VSMC proliferation, and arterial remodeling [35,36]. Since early arterial thrombosis, stenosis, and adverse remodeling are associated with early AVF fail- ure in the presence of chronic inflammation [12], considering the mediators regulating arterial remodeling, along with outflow vein remodeling, will be of significance in en- hancing vessel and AVF patency. VSMC proliferation plays a crucial role in the pathogenesis of plaque formation and NIH and cyclin D1 play a critical role [37]. Furthermore, the attenuation of adverse vas- cular remodeling via CCND1 inhibition supports the therapeutic potential of targeting CCND1 [38]. The increased expression of CCND1 observed in thrombosed vessels (Table Proteomes 2022, 10, 14 17 of 24 6) in this study indicates that CCND1 might be a therapeutic target to enhance AVF mat- uration by attenuating NIH and vessel stenosis. The ETS family of transcription factors plays a critical role in plaque formation and vulnerability and increased expression of Ets- 1 in VSMCs is associated with plaque vulnerability [39], vascular inflammation, and re- modeling [40]. An increased expression of ETS and SPI1 (the transcription factor PU.1) in the thrombosed arteries in this study suggest their role in early thrombosis, mediating early AVF failure. VSMC proliferation and NIH are also regulated by GATA4-mediated cyclin D1 transcription [41] and we observed activated GATA4 in our study (Table 6). Interferon regulatory factor (IRF) 1, which was also found to be activated in this study, is another TF regulating neointima formation after intimal injury and preventing NIH, in- volving the Ang-II receptor and attenuating vascular remodeling [42]. On the other hand, IRF9, a transcription factor that was found to be activated in this study, is essential for neointima formation and VSMC proliferation after vascular injury [43]. EC proliferation is regulated by another TF, namely, MEF2 (myocyte enhancer factor 2) and we observed activated MEF2C (Table 6) in our study. MEF2 is an upstream regulator of several tran- scription factors and it promotes an anti-inflammatory and antithrombotic endothelium by regulating TFs, including Klf2, Klf4, and Notch [44]. This suggests the critical role of MEF2C in vessel thrombosis and it might be a therapeutic target for enhancing vessel pa- tency and AVF maturation. This notion is supported by the activated NOTCH4 observed in this study. CITED2 (CBP/p300-interacting transactivator with ED-rich tail 2) suppresses genes mediating angiogenesis and ECM remodeling and regulates the expression of various MMPs [45] and HIF-1α [46] and regulates inflammatory genes involved in fibrosis [47,48]. HIF-1 α, angiogenesis, inflammation, and ECM remodeling play a critical role in AVF maturation and failure, and the regulation of CITED2 expression may play a therapeutic role. The decreased expression of CITED2 observed in this study suggests its probable role in thrombosis and stenosis; however, its role in AVF maturation and failure warrant investigation. ECM remodeling is mediated by various MMPs and the activation of NF- κB regulates the expression of MMPs. The change in hemodynamics associated with shear stress activates NF-κB and regulates long-term flow-induced vascular remodeling [49]. The activated NF-κB2 (nuclear factor Kappa B subunit 2), REL-A (RELA proto-oncogene, NF-κB subunit), and REL-B (RELB proto-oncogene, NF-κB subunit) observed in this study suggest the role of NF-κB in inflammation [12], stenosis, and remodeling, and targeting NF-κB might have therapeutic potential. Network analysis with the transcription factors as inputs revealed molecular mechanisms, including macrophage polarization, the in- volvement of reactive oxygen species, fibrosis, cell cycle, apoptosis, mitochondrial biogen- esis, adipogenesis, and lipogenesis, as well as the interaction of various transcription fac- tors with DEGs (Figure 3). Network analysis also revealed the involvement of FOXO genes and this finding is important because FOXO4 plays a critical role in early inflam- matory responses after intimal injury and in apoptosis [50,51]. Proteomes 2022, 10, 14 18 of 24 Figure 3. Network analysis with TFs as inputs revealed molecular mechanisms associated with in- flammation, hypoxia, adipogenesis, lipogenesis, and mitochondrial biogenesis associated with early thrombosis. Blue circles—input transcription factors, red circles—differentially expressed genes, and yellow squares—molecular mechanisms associated with early thrombosis in arteries involved in arteriovenous fistula. NIH is a major contributor of arterial restenosis. Vitamin D, exerting its biological effect through the vitamin D receptor (VDR), plays a critical role in mitigating inflamma- tion and excessive NIH [52–54]. VDR was inhibited in this study, and this might be due to the presence of chronic inflammation [12]. Decreased expression of VDR is correlated with arterial thrombosis and as per previous reports. The CC chemokine CCL5/RANTES (regulated on activation normal T cell expressed and secreted), which is expressed in VSMCs, regulates NIH, and CCL5 expression in smooth muscle cells is regulated by tran- scription factor YBX-1, which was found to be activated in this study, coding for Y-box binding protein-1 [55]. Chen et al. [56] reported the role of EHMT2 (also known as G9a) in the regulation of aortic smooth muscle cell death by suppressing autophagy activation independently of proliferation and apoptosis. Histone lysine methyltransferase EHMT2 also plays a role in Proteomes 2022, 10, 14 19 of 24 vasculopathy and vascular inflammation [57]. Another TF, GATA1, regulates EC function and angiogenesis by regulating AGGF1 [58]. Increased expression of HDAC4 is associated with vascular inflammation and associated inflammatory diseases via the activation of autophagy, and knockdown of HDAC4 ameliorates vascular inflammation [59]. We ob- served HDAC4 to be inhibited in the LR12 + TAK-242-treated arteries and activated in scrambled and ethanol-treated arteries in this study, and this might be the effect of the treatment given to the swine. Similarly, the inhibition of HDAC5 is associated with the reduction of angiotensin II-induced vascular contraction, hypertrophy, and oxidative stress in a mouse model [60], and HDAC5 was found to be inhibited (Table 6) in this study, as an effect of the treatment given. Investigating the role of epigenetic regulators including HDACs and H3K9me3 is of significance because they have abnormal mechanoresponses to arterial laminar shear stress and play a role in EC damage [61], with etiologies playing a critical role in vessel remodeling in AVF. miRNAs regulate gene expression epigenetically and miR-132 upregulation is asso- ciated with reduced VSMC proliferation by attenuating the expression of LRRFIP1 and thus attenuating neointima formation [62]. The transcription factor LRRFIP1 was acti- vated (Table 6), and miR-132 appeared in the data (Supplementary File S1) but was neither activated nor inhibited. The activation of LRRFIP1, a target of miR-132, in thrombosed and stenosed arteries in this study, is indicative of excessive VSMC proliferation. An at- tenuated expression of mir-133 is associated with the phenotypic switching, proliferation, and migration of VSMCs and thereby plays a role in vascular remodeling [63]. miR-155, by downregulating the soluble guanylyl cyclase (sGC/cGMP) pathway, negatively regu- lates VSMC functions that are essential for maintaining the VSMC contractile phenotype and vasorelaxation [64]. miR155-5p, which was found to be inhibited in this study, sup- presses ACE expression and its downstream production of Ang II, superoxide anions, and inflammatory factors, and it inhibits VSMC migration and oxidative stress in spontane- ously hypertensive rats but has no effects on VSMC migration with exogenous Ang II [65]. miR155-5p in adventitial fibroblast-derived extracellular vesicles inhibits the proliferation of VSMCs [66]. Since oxidative stress, hypoxia, and VSMC proliferation play a critical role in AVF maturation/failure, these miRNAs might play an important role. The levels of miR- 34a-5p, associated with atherosclerosis and miR-34a-5p, play a crucial role in inflamma- tory pathology [67] and various studies [68,69] have discussed miR-34a-5p, which was found to be inhibited in this study, as a potential therapeutic target in cardiovascular dis- ease. miR-205-5p, which was found to be activated in this study, suppresses the prolifer- ation of pulmonary VSMCs [70], whereas miR-153, which was found to be inhibited in this study, attenuates hypoxia-induced excessive proliferation and migration of pulmo- nary arterial SMCs [71]. miR-22, which was found to be inhibited in this study, regulates VSMCs apoptosis (through p38MAPKα) and vascular remodeling in aortic dissection [72], whereas miR-219, which was found to be inhibited in this study, is associated with vascu- lar ischemia during cerebral artery occlusion [73] (Tables 1 and 2). The expression of these miRNAs in this study and their relationship with vascular pathologies suggest that these miRNAs may be potential therapeutic targets in AVF. Other miRNAs which have been described in relation to vascular remodeling, including miR-1 and miR-204 [74], also ap- peared in this study but were neither activated nor inhibited. Additionally, this study re- vealed some novel miRNAs which have not been discussed in the literature (Supplemen- tary File S1) in relation to arterial stenosis and thrombosis involved in AVF and which may cause early AVF maturation failure and thus might have therapeutic potential, since microRNAs play a critical role in AV shunting, angiogenesis, thrombosis, restenosis, NIH, and vessel remodeling [75–78]. 5. Future Perspectives Overall, the results of this study revealed various TFs and microRNAs associated with early arterial thrombosis, stenosis, and nonpatent AVF. The role of various TFs and Proteomes 2022, 10, 14 20 of 24 microRNAs has been discussed in the literature in association with inflammation; the pro- liferation and migration of VSMCs; the proliferation and dysfunction of ECs; ECM remod- eling; and angiogenesis in terms of vascular remodeling, as discussed above. However, the roles of these TFs and microRNAs and others that appeared in this study have not been investigated in relation to AVF maturation and maturation failure. The involvement of other TFs and microRNAs found in this study has been discussed in relation to embry- onic organogenesis, vasculogenesis, cardiac remodeling, etc. (Supplementary File S3) this but warrants further research in association with AVF. For example, NRIP1, which was found to be activated in this study, also appeared in another data set [79] in association with blood coagulation, ECM remodeling, inflammatory responses, the TGF signaling pathway, and angiogenic proteins, and all these play a role in AVF maturation and failure. Similarly, TF NUPR1 encoding nuclear protein 1 (Nupr1), a marker for phenotypic mod- ulation (modSMCs) towards a fibroblast-like state, which was found to be activated in this study, also appeared in another single-cell transcriptomic profile of VSMC phenotypes [80]. PPARGC1A, a TF that was observed to be inhibited in this study, also appeared in another genomic and transcriptomic analysis highlighting the role of vascular changes in multiple sclerosis—mainly immune and inflammation-related pathologies mediating perivascular changes [81]. Since perivascular cuffing and perivascular inflammation are associated with arterial thrombosis [12], investigating the role of PPARGC1A will enhance the understanding of the molecular mechanism underlying early AVF failure. SMARCC1 (Swi/snf-related, matrix-associated, actin-dependent regulator of chromatin subfamily c member 1) and SMARCA4 (Swi/snf-related, matrix-associated, actin-dependent regulator of chromatin, subfamily a, member 4) was found to be activated in this study, and Brg1, a central catalytic subunit of the SWI/SNF apparatus, plays a critical role epigenetically in VSMC proliferation [82]. ZNF423 plays a role in activating autophagy by binding to BCAT1 in hypoxic pulmonary artery smooth muscle cells [83], since hypoxia occurs after AVF creation due to disruption of the vasa vasorum. Therefore, ZNF423 might play an important role in AVF maturation and maturation failure. Furthermore, the protein–pro- tein interactions between the genes regulated by these TFs support the notion of their role in early thrombosis, and targeting them might be of therapeutic significance in AVF. 6. Conclusions The results of this study revealed various transcription factors and microRNAs, of which some have implications in vascular remodeling, NIH, VSMC proliferation and mi- gration, and EC proliferation and function (as discussed) and some have been discussed in the literature in other contexts (Supplementary File S3). However, none of these TFs and microRNAs have been investigated in relation to early thrombosis, stenosis, vessel patency, AVF maturation, and maturation failure after AVF creation and thus warrant further research. Since genetic and epigenetic regulation of genes is an evolving research area, investigating genetic and epigenetic mediators playing a critical role in AVF matu- ration failure and targeting them will be of great significance to improving clinical out- comes. Supplementary Materials: The following supporting information can be downloaded at: www.mdpi.com/article/10.3390/proteomes10020014/s1, Supplementary File S1: List of all mi- croRNAs; Supplementary File S2: List of all transcription factors; Supplementary File S3: Details of transcription factors. Author Contributions: Conceptualization, V.R.; methodology, V.R.; software, V.R.; validation, V.R. and D.K.A.; formal analysis, V.R.; investigation, V.R.; resources, D.K.A.; data curation, V.R.; writ- ing—original draft preparation, V.R.; writing—review and editing, D.K.A.; visualization, V.R.; su- pervision, D.K.A.; project administration, D.K.A.; funding acquisition, D.K.A. All authors have read and agreed to the published version of the manuscript. Funding: V.R. is supported by an intramural grant IMR Rai 12397B from the Western University of Health Sciences, Pomona, California. The research work of D.K.A. is supported by the R01 Proteomes 2022, 10, 14 21 of 24 HL144125 and R01 HL147662 grants from the National Institutes of Health, USA. The content of this critical review is solely the responsibility of the authors and does not necessarily represent the offi- cial views of the National Institutes of Health. Institutional Review Board Statement: The Institutional Animal Care and Use Committee (IACUC) at Western University of Health Sciences approved protocol No. R20IACUC038 for this study. Informed Consent Statement: Not applicable. Data Availability Statement: All data supporting the results of this manuscript have been included in this manuscript along with Supplementary Files. Bulk RNA seq (Fastq. 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DOI: 10.3390/proteomes10020014
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Abstract

Article Transcriptional and Epigenetic Factors Associated with Early Thrombosis of Femoral Artery Involved in Arteriovenous Fistula Vikrant Rai and Devendra K. Agrawal * Department of Translational Research, Western University of Health Sciences, Pomona, CA 91766-1854, USA; vrai@westernu.edu * Correspondence: dagrawal@westernu.edu; Tel.: +1-909-469-7040; Fax: +1-909-469-5577 Abstract: Arteriovenous fistulas (AVFs), created for hemodialysis in end-stage renal disease pa- tients, mature through the outward remodeling of the outflow vein. However, early thrombosis and chronic inflammation are detrimental to the process of AVF maturation and precipitate AVF matu- ration failure. For the successful remodeling of the outflow vein, blood flow through the fistula is essential, but early arterial thrombosis attenuates this blood flow, and the vessels become throm- bosed and stenosed, leading to AVF failure. The altered expression of various proteins involved in maintaining vessel patency or thrombosis is regulated by genes of which the expression is regulated by transcription factors and microRNAs. In this study, using thrombosed and stenosed arteries fol- lowing AVF creation, we delineated transcription factors and microRNAs associated with differen- tially expressed genes in bulk RNA sequencing data using upstream and causal network analysis. We observed changes in many transcription factors and microRNAs that are involved in angiogen- esis; vascular smooth muscle cell proliferation, migration, and phenotypic changes; endothelial cell function; hypoxia; oxidative stress; vessel remodeling; immune responses; and inflammation. These Citation: Rai, V.; Agrawal, D.K. factors and microRNAs play a critical role in the underlying molecular mechanisms in AVF matu- Transcriptional and Epigenetic ration. We also observed epigenetic factors involved in gene regulation associated with these mo- Factors Associated with Early lecular mechanisms. The results of this study indicate the importance of investigating the transcrip- Thrombosis of Femoral Artery tional and epigenetic regulation of AVF maturation and maturation failure and targeting factors Involved in Arteriovenous Fistula. precipitating early thrombosis and stenosis. Proteomes 2022, 10, 14. https:// doi.org/10.3390/proteomes10020014 Keywords: arteriovenous fistula; early thrombosis; epigenetic regulation; maturation failure; Academic Editor: Sixue Chen microRNAs; transcription factors; transcriptional regulation Received: 18 March 2022 Accepted: 28 April 2022 Published: 30 April 2022 1. Introduction Publisher’s Note: MDPI stays neu- tral with regard to jurisdictional Arteriovenous fistulas (AVFs) provide vascular access for hemodialysis in patients claims in published maps and institu- with end-stage renal disease [1]. The successful use of an AVF for hemodialysis depends tional affiliations. on the maturation of the fistula through the outward remodeling of the outflow vein to make it appropriate to respond to increased blood flow so that it can be repeatedly can- nulated to provide adequate flow for dialysis [2,3]. The maturation rates of AVFs have been reported to range between 40% and 80%, but these rates decrease with aging, a distal Copyright: © 2022 by the authors. Li- fistula location, and a small vein diameter [4]. Non-maturation of AVF due to early throm- censee MDPI, Basel, Switzerland. bosis, chronic inflammation, and the failure of outward remodeling is an important cause This article is an open access article of AVF failure. Early thrombosis can occur due to hematoma formation, a hypercoagula- distributed under the terms and con- tion state, decreased blood flow rates, intimal injury during AVF creation, and endothelial ditions of the Creative Commons At- cell dysfunction, leading to chronic inflammation and the failure of outward remodeling tribution (CC BY) license (https://cre- [2]. Along with thrombosis, stenosis and neointimal hyperplasia (NIH) of the outflow vein ativecommons.org/licenses/by/4.0/). also contribute to early AVF failure [5,6]. Chronic inflammation involving various medi- ators of inflammation, including triggering receptor expressed on myeloid cells-1 (TREM- Proteomes 2022, 10, 14. https://doi.org/10.3390/proteomes10020014 www.mdpi.com/journal/proteomes Proteomes 2022, 10, 14 2 of 24 1) and toll-like receptor 4 (TLR4), plays a crucial role in NIH, thrombosis, and stenosis [7– 11]. To attenuate vessel stenosis after AVF creation, we targeted chronic inflammation by inhibiting TREM-1 using LR-12 peptides and TLR4 with TAK-242 and performed se- quencing in collected tissues to investigate the changes in gene expression associated with stenosis and thrombosis. Recently, we reported on differentially expressed genes (DEGs) associated with stenosis and thrombosis of the femoral artery involved in AVF [12]. The presence of differentially expressed genes in the thrombosed artery that are in- volved in AVF indicates the change in gene expression associated with AVF creation and failure due to thrombosis. Since the expression of genes is regulated at both transcriptional and post-transcriptional levels [13], we investigated the transcription factors and mi- croRNAs associated with either activated or inhibited networks and regulating the ex- pression of various DEGs expressed in our data set (hereafter referred to as activated or inhibited). Transcription factors (TFs) are regulatory proteins involved in the process of convert- ing DNA to RNA (transcription) that allow the unique expression of a gene in different cell types. The function of TFs is to activate the transcription of DNA but TFs rarely inhibit gene expression [13]. MicroRNAs (miRs) belong to the class of small non-coding RNAs (18- to 25 nucleotide long), which are involved in controlling gene expression post-tran- scriptionally by targeting mRNAs based on sequence complementarity [14,15]. An miRNA binds with its target messenger RNA (mRNA) and blocks its translation or pro- motes its degradation, thereby decreasing gene expression. However, in opposition to the consensus that miRNA reduces gene expression, there is evidence that some miRNAs can upregulate gene expression [16,17]. Furthermore, TFs, including nuclear factor kappa beta (NF-κB), PU.1, the Ets-1 family, activator protein 1 (AP1), Krϋppel-like factor 2 (KLF2), zinc fingers and homeoboxes 2 (Zhx2), and activating transcription factor 4 (ATF4), play a critical role in atherosclerosis [18]. Since TFs and miRNAs regulate gene expression, tar- geting TFs and miRNAs to modulate gene expression favoring attenuated thrombosis, stenosis, and plaque formation will be of therapeutic importance. Decreased thrombosis, stenosis, and excessive NIH will render arteries and veins patent, and this may increase AVF patency. In this study, we have revealed various novel TFs and miRNAs associated with early thrombosis of the femoral artery involved in AVF and regulating various dif- ferentially expressed genes (DEGs) associated with early thrombosis. Thus, targeting these TFs and miRNAs might be of therapeutic and clinical significance. 2. Materials and Methods The material and methods involved in creating AVFs, assessing pre-and post-opera- tive femoral vessels involved in AVFs, tissue harvesting, tissue processing, histology, im- munostaining, and bulk RNA sequencing have been described previously [12]. We choose the swine model because the swine model represents the best choice for studies of occlu- sive arterial and venous diseases and there are similarities in the anatomy, physiology and pathological responses of the human and porcine cardiovascular systems [19]. Addition- ally, this approach enables us to use the same catheters and tools as those used clinically in humans. Female swine were chosen because female swine are easy to handle compared to male swine and because of the fact that, due to their aggressive behavior, nearly 100% of male pigs have been castrated chemically or immunologically, and such procedures change the hormonal pattern and pathophysiological responses that could significantly affect the outcomes and results due to their effect on immune and resident cells. Briefly, following the creation of AVFs in Yucatan miniswine, LR12 (TREM-1 antagonist) + TAK242 (TLR4 antagonist) were locally administered at the site of fistula creation. For controls, a mixture of scrambled peptide +30% ethanol as the vehicle of TAK-242 was used. LR12 was injected once during the surgery and >10 lentiviral particles in 1 mL were injected. TAK-242 was administered at a dose of 3 mg/kg as bolus and then 0.1 mg/kg daily for 7 days and then once weekly for 4 weeks as a maintenance dose. The patency of Proteomes 2022, 10, 14 3 of 24 the femoral artery and vein was assessed with doppler ultrasound, angiography, and op- tical coherence tomography at the end of 12 weeks and the swine were sacrificed for the harvesting of tissues. Femoral arteries from the AVF site and from the contralateral sides were collected. The total RNA, isolated using the TRIzol method from three thrombosed femoral arteries and three contralateral femoral arteries, was sent for bulk RNA sequenc- ing. RNA samples with RIN > 6 were subjected to library preparation and sequencing using the NEBNext Ultra II RNA Library Prep Kit for Illumina and Illumina HiSeq instru- ments at Genewiz LLC (Plainfield, NJ, USA) as mentioned in [12]. IPA was used for the analyses of microRNAs and transcription factors, as detailed in the following sections. 2.1. Ingenuity Pathway Analysis In order to perform the functional analysis to investigate the novel transcription fac- tors and microRNAs, ingenuity pathway analysis (IPA) was conducted by uploading the identified genes from bulk RNA sequencing. The functional IPA analysis and the statisti- cal inference was completed at Bioinformatics and Systems Biology Core at the University of Nebraska Medical Center (UNMC, Omaha, NE, USA). IPA pathway analyses, includ- ing causal network and regulator effect analyses, were performed using IPA (QIAGEN Inc.,Germany, https://digitalinsights.qiagen.com/products-overview/discovery-insights- portfolio/analysis-and-visualization/qiagen-ipa/ (accessed on 31 August 2021)) . Causal network analysis was conducted to examine causal relationships associated with input genes by expanding the upstream analysis to include regulators that are not directly con- nected to targets in the dataset, as well as to identify potential therapeutic or toxicity tar- gets and known drugs and biomarkers. The analysis of regulator effects provides unprec- edented insights into the input data by integrating upstream regulator results with the results of the downstream effects to create causal hypotheses. The results of regulator ef- fects analysis explain the way in which the events occurring upstream cause a particular phenotypic or functional outcome downstream (https://digitalinsights.qiagen.com (ac- cessed on 31 August 2021)). 2.2. Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) Network Analysis Transcription factors regulate the expression of a gene, which in turn regulates pro- tein expression through translation. To delineate the protein–protein interactions regu- lated by the TFs that appeared in our data set, we performed STRING network analysis using https://string-db.org/ (accessed on 19 February 2022). 3. Results IPA analysis revealed microRNAs associated with activated and inhibited networks. The upstream regulator analysis using input genes from the LR12 + TAK242-treated group compared to the contralateral femoral arteries revealed 15 microRNAs (mir-193, mir-365, miR-199a-5p, miR-889-3p, mir-889, mir-1, mir-137, miR-1195, mir-644, miR-293-5p, mir- 122, miR-224-5p, mir-153, mir-346, and mir-467; mir-microRNA, and miR-mature mi- croRNA) but none were associated with activated or inhibited networks. The causal net- work analysis using input genes from the LR12 + TAK242-treated group compared to the contralateral femoral arteries revealed nine microRNAs (miR-889-3p, mir-889, mir-365, miR-199a-5p, mir-15, mir-379, miR-23a-3p, mir-193, miR-96-5p; mir-microRNA, and miR- mature microRNA) but these were not associated with activated or inhibited networks. The upstream regulator analysis using input genes from the LR12 + TAK242-treated group compared to the scrambled peptide + ethanol group arteries revealed 35 microRNAs, and of these, three were inhibited (Table 1) and 32 microRNAs (mir690, miR-1258, miR-690, mir-1258, mir-129, mir-219, mir-363, mir-584, mir-132, miR-150-5p, miR-375-3p, mir-142, mir-204, miR-30c-5p, miR-204-5p, miR-4728, miR-101b-3p, miR-185-3p, miR-4728-3p, mir- 331, mir-663, mir-210, mir-451, mir-33, miR-138-5p, miR-483-3p, mir-155, mir-8, mir-30, Proteomes 2022, 10, 14 4 of 24 mir-17, mir-223, and miR4269; mir-microRNA, and miR-mature microRNA) were not as- sociated with activated or inhibited networks. The causal network analysis, using input genes from the LR12 + TAK242-treated group compared to scrambled peptide + ethanol group arteries, revealed 17 microRNAs and, of these, one microRNA was associated with an activated network and four microRNAs were associated with inhibited networks (Ta- ble 2), whereas 12 microRNAs (mir-124, mir-326, mir-181, mir-132, miR-4269, miR-138-5p, miR-1258, miR-690, mir-1258, mir-219, mir-584, miR-690; mir-microRNA, and miR-mature microRNA) were not associated with activated or inhibited networks. The identified mi- croRNAs were associated with various differentially expressed genes (DEGs) with log2 > 2 or <−2 with p < 0.05 and other DEGs with log2 < 2 or >−2, p < 0.05. From the list of all DEGs, we sorted out the DEGs involved in NIH, plaque formation, endothelial cell (EC) dysfunction, phenotypic changes in VSMCs, proliferation and migration of ECs and VSMCs, angiogenesis, vasculogenesis, immune cell recruitment, and inflammation based on an extensive literature search (the complete list of microRNAs can be found in Supple- mentary File S1). Table 1. MicroRNAs revealed with upstream network analysis: LR12 + TAK242-treated vs. scram- bled peptide and vehicle (30% ethanol)-treated arteries. DEGs with higher expression in scrambled peptide-treated group (log2 > 2, p < 0.05) and with higher expression in LR12 + TAK242-treated group (log2 <−2, p < 0.05). Associated with Inhibited Network DEGs (log2 > 2, p < 0.05) DEGs (log2 <−2, p < 0.05) mir-133 VCAN PPARG, TNFSF10 miR-155-5p BACH1, CD69, CXCL8, PMAIP1, PPL, RIPK1 Table 2. MicroRNAs revealed with causal network analysis: LR12 + TAK242-treated vs. scrambled peptide and vehicle (30% ethanol)-treated arteries. DEGs with higher expression in scrambled pep- tide-treated group (log2 > 2, p < 0.05) and with higher expression in LR12 + TAK242-treated group (log2 < −2, p < 0.05). Associated with DEGs (log2 < 2 Activated DEGs (log2 > 2, p < 0.05) DEGs (log2 < −2, p < 0.05) or >−2, p < 0.05) Network ADAM8,ADAMTS4,ARG1,CD27,CD3D, ADAMTS8,ADIPOR2,AQP11,BCAM, CD5,CSF2RB,CXCL8, DNAH11,DUOX2, CAT,CEBPA,CIDEC,DHH,EIF4EBP1, GP1BA,GP1BB,HPSE,IL1R1,IL2RB,IL7R, miR-205-5p HCAR1,LMO2,MYLPF,PLIN1,PLIN5, CD6, CXCR5, EGR2 KDM6B,LAG3,MMP25,NCOR2,PCDH8, PNPLA2,PPARG,RETSAT,SEMA3G, PHACTR1,PPRC1,PTX3,RELT,SEMA4A, SERPINI1,TEK,WNT11 SERPINB2, STAT4,STEAP4 Associated with inhibited networks ADAM8,ADAMTS4,ALAS2,BCL3,CCR7, CD27,CD3D,CD69, COL6A3,CXCL8, DUOX2, AGT,ALDH9A1,APOD,APOE,ASS1, ECM1,EDIL3,EGR1,FCMR, BCAM,BMX,CAVIN2,CIDEC,CLEC14A,C FERMT3,GP1BA,GPR84, HPSE,ICOS, YP4B1,DDIT4L,ECH1,EIF4EBP1, IL2RB,IL7R, KDM6B,LAG3,MAP3K14, mir-515 ENHO,EPHX2,HCAR1,MMD,MYH4, CD6, CXCR5 MUC13,MYC,NCOR2,PCDH8,PHACTR1, MYL1,OSM,OSMR,PPARG,PLIN5, PLAGL1,PPRC1,PRDM1,PTX3,RCAN1, PNPLA2,PON3,S100A1,SERPINI1, RNF213, SBNO2, SELP, SEMA4A, SERPINB2, TCAP,TEK,TMOD4,TNNC2, TSPAN5 SLAMF7,ST18,STAT4,STC1, TUBB1,VWF ADAMTS1, CD5,CD69,CSF2RB, CXCL8, AGT,ALDH1A1,APOD,APOE,BCAM, DUOX2,EGR3,FCMR,GP1BA,HAS1,ICOS,IL2RB DHH,MYH8,MYL1,MYOT,OSM, mir-153 ,ITGA2B,ITGB7,KDM6B,NCOR2, OSMR,PLIN1,RAMP2,RETSAT, NFATC2,PDE4B,PHACTR1,PRDM1, RNF213, S100A1,SCD,TCAP,TNNC2,WNT11 SERPINB2,ST18,STAT4,STEAP4, TEAD3, VDR ALAS2, BCL3,BLK,CCL22,CSF2RB, ALDH1A1,APOD,APOE,ASS1,BCAM, mir-219 EGR2, IL9R CXCL8,DUOX2,FCMR,ICOS,IL2RB,IL7R, CAT,CAVIN2,LDHB,PPARG,MMD, Proteomes 2022, 10, 14 5 of 24 ITGA2B, NFATC2, PDE4B, PHACTR1, MYL1,MYLPF,MYOT,OSM,PEX11A, PLAGL1, PRDM1,PTX3, RNF213, SBNO2, RAMP1,TEK STEAP4,TUBB1,VWF CCL22,CCR7,DUOX2,HAS1,IL1R1,IL2RB,IL7R,I mir-22 TGA2B,ITGB7, NFATC2,PTX3, ALDH1A2,ASS1,BCAM, MYLPF, TNNC2 EGR2 RCAN1,SERPINB2,STEAP4, VDR IPA analysis revealed transcription factors associated with activated and inhibited networks. The upstream regulator analysis using input genes from the LR12 + TAK242- treated group compared to contralateral femoral arteries revealed 132 TFs (Supplemen- tary File S2 sheet 1). Among 132 TFs, 11 TFs were associated with activated networks, whereas four TFs were associated with inhibited networks (Table 3). The causal network analysis using input genes from the LR12 + TAK242-treated group compared to contrala- teral femoral arteries revealed 91 TFs (Supplementary File S2 sheet 2) and of these, 13 were associated with activated and 14 were associated with inhibited networks (Table 4). The upstream regulator analysis using input genes from the LR12 + TAK242-treated group compared to scrambled peptide + ethanol group arteries revealed 311 TFs (Supplementary File S2 sheet 3) and of them, 14 were associated with activated and 11 TFs were associated with inhibited networks (Table 5). The causal network analysis using input genes from the LR12 + TAK242-treated group compared to scrambled peptide + ethanol group arter- ies revealed 152 TFs (Supplementary File S2 sheet 3) and of these, 22 TFs were associated with activated and 25 TFs were associated with inhibited networks (Table 6). The identi- fied TFs were associated with various differentially expressed genes (DEGs) with log2 > 2 or <−2 with p < 0.05 and other DEGs with log2 < 2 or >−2, p < 0.05. From the list of all DEGs, we sorted out the DEGs involved in NIH, plaque formation, endothelial cell (EC) dysfunc- tion, phenotypic changes in VSMCs, proliferation and migration of ECs and VSMCs, an- giogenesis, vasculogenesis, immune cell recruitment, and inflammation based on an ex- tensive literature search (the complete list of TFs can be found in Supplementary File S2). Table 3. Transcription factors revealed with upstream network analysis: LR12 + TAK242-treated vs. contralateral femoral arteries. DEGs with higher expression in LR12 + TAK242-treated group (log2 > 2, p < 0.05) and with higher expression in contralateral femoral arteries (log2 < −2, p < 0.05). Associated with Activated DEGs (log2 > 2, p < 0.05) DEGs (log2 <−2, p < 0.05) DEGs (log2 < 2 or >−2, p < 0.05) Networks MYOD1 CXCL14,MYH4,MYL1,MYLPF,MYOD1,TNNC2 IGF1 SMARCA4 IL7R,ITGA3, MYL1,MYLPF,TNNC2 CNTN1, MAP1B, MFGE8, IGF1 MEF2C MYLPF,MYOD1,MYOT FRZB, KCNA5, IGF1 MYF6 EGF,IL15,IL7,MYOD1, HOXC8 IL1R2,SLC16A3 SPDEF ITGA3 TNC COL4A2 SPI1 CD1D,CXCL14,IL18,IL1R2,IL7R CD79B IRF1 IL15,IL18,IL7,MMP9 RB1 HOMER1,MYH4,MYH8,MYL1,TCAP,TNNC2 IGF1, COL4A2 ARNT2 APOE,EGF,HOMER1 SIM1 APOE,EGF,HOMER1 Associated with inhibited network HDAC4 HOMER1,MMP9,MYLK2,MYOT CNTN1 KDM5A HOMER1,MYH4,MYH8,MYL1,TCAP,TNNC2 HTT APOE,HOMER1,IL15,MYL1,MYOD1, PRELP COL4A2 VDR IL18,MYH8,S100A8 COL4A2 Proteomes 2022, 10, 14 6 of 24 Table 4. Transcription factors (TFs) revealed with causal network analysis: LR12 + TAK242-treated vs. contralateral femoral arteries. DEGs with higher expression in LR12 + TAK242-treated group (log2 > 2, p < 0.05) and with higher expression in contralateral femoral arteries (log2 < −2, p < 0.05). Associated with DEGs (log2 < 2 DEGs (log2 > 2, p < 0.05) DEGs (log2 < −2, p < 0.05) Activated Networks or >−2, p < 0.05) ANK1,APOE, CASQ1,EGF,HOMER1,IL15,IL18,IL7R, MYF6 AQP5, NOTCH3, TNC IGF1 MYL1,MYLPF,MYOD1,MYOT,TCAP,TNNC2,TRIM63 ANK1,APOE,CASQ1,EGF,HOMER1,IL18,IL7R,MYL1, MYOD1 AQP5, NOTCH3, TNC IGF1 MYLPF,MYOT, TCAP, TNNC2,TRIM63 AMPD1,APOE,CD1D,CD247,CD3G,CYGB,DDIT4L, ADAMTSL4, AEBP1, EGF,FGR, HABP2,ICAM3,IL15,IL1R2,IL7R, MMP9, COL8A1, ELN, FBLN5, NCOA4 IGF1, COL4A2 MMRN1,MYH8,MYLPF,MYOT,MYPN, FRZB, KCNA5, MAP1B, PRKCQ,S100A1, SLN,TFPI,TNNC2,XIRP2 MFGE8, NOTCH3, SLIT3 ANK1, CNKSR1,CXCL14,DOK5,EGF, HOMER1, AEBP1,AQP5,COL8A1, ICAM3,IFIT1,IL15,IL18,IL1R2,IL7,IL7R, MYH4,MYH8, NCOA1 ELN,FBLN5,FMOD, ITGA3, COL4A2 MYL1,MYOT,MYPN,PLIN5,STK17B,TCAP,TNNC2, MAP1B, NOTCH3, SLIT3 TRIM63,XIRP2 CD1D,CD247,CNKSR1,CXCL14,CYGB,DDIT4L, ADAMTSL4, BGN, ELN, FGR, HOMER1,ICAM3,IFIT1,IL15,IL18,IL7, COL8A1,FBLN5,FMOD, IGF1, COL4A2, FOXA1 MMP9,MYH4,MYH8,MYL1,MYLPF,MYOD1,MYPN, FRZB,ITGA3,MAP1B,TNC,N CD79B PLIN5, PRKCQ,S100A1, STK17B,TCAP,TNNC2,XIRP2 OTCH3, PRELP,SLIT3 ITGA3, MAP1B, IGF1,CD79B SMARCC1 CNKSR1,ICAM3,IL7R, MMP9,MYOT NOTCH3,TNC COL4A2 SMARCA4 IFIT1,MYL1,MYLPF,TNNC2 CNTN1 IGF1 HOXC8 IL1R2,SLC16A3 SPDEF ITGA3,TNC COL4A2 IRF9 IFIT1,IL18 Associated with inhibited networks CD1D,CD247,CD3G,CXCL14, HABP2, ICAM3, IFIT1, AEBP1,FBLN5,FMOD, IL15,IL18,IL1R2,IL7R, MYH4,MYH8,MYL1,MYLK2, AHRR FRZB, MFGE8, NOTCH3, COL4A2 MYOT,MYPN,PLIN5,PRKCQ, STK17B,TCAP,TRIM63, SLIT3 XIRP2 ANK1, CXCL14,DOK5, HABP2, IFIT1,IL15,IL18, AQP5,BGN,COL8A1, HDAC3 IL7,IL7R, MYH8,MYLPF, PLIN5, S100A8,SLN, ELN,FBLN5,ITGA3, IGF1, COL4A2 TFPI,TRIM63 KCNA5,NOTCH3, RASL11B CNKSR1,CXCL14,CYGB,DDIT4L,DOK5,EGF,FGR, ADAMTSL4,ADRA1D, HOMER1,ICAM3,IL15,IL18,IL7,MMP9,MYH4,MYH8,MYL1 AEBP1,AQP5,BGN, FBLN5, EID1 IGF1 ,MYOT,MYPN,PLIN5,S100A8, STK17B,TMOD4, FRZB,KCNA5,MAP1B, TNNC2,XIRP2 NOTCH3, SLIT3, TNC APOE,CNKSR1,CXCL14,CYGB,DDIT4L,DOK5,EGF, ADAMTSL4,ADRA1D,AEBP FGR,HOMER1,ICAM3,IL15,IL18,IL7,MMP9,MYH4, 1, FBLN5, FRZB, HOXA10 CD79B MYH8,MYL1,MYOT,MYPN,PLIN5,S100A8, KCNA5,MAP1B, NOTCH3, TK17B,TMOD4, TNNC2,XIRP2 SLIT3, TNC CD1D,COL8A1,CXCL14,DOK5,ICAM3,IFIT1,IL15, MTA2 IL18,IL1R2,IL7R, MMP9,MYH4,MYH8,MYL1,MYLPF, AQP5, ELN,FBLN5, MAP1B, MYOD1,MYOT,PLIN5,S100A8,SLC16A3,TNNC2, TRIM63 ADAMDEC1, C5AR2,CNKSR1,CXCL14,CYGB, ADRA1D,AEBP1, FBLN5, DOK5, FGR, ICAM3,IL18,IL7R, MYH4,MYH8,MYL1, FRZB,ITGA8,KCNA5, HOXA9 CD79B MYOD1,MYOT,MYPN,PLIN5,PRKCQ,S100A8, STK17B, MAP1B, NOTCH3, SLIT3, TNNC2,TRIM63,XIRP2 TNC CD3G,IL7R,MMP9,MYL1,MYLPF,MYOD1, STK17B, TCF3 NOTCH3 IGF1, CD79B TNNC2 CD247,CD3G, IL15,IL1R2,IL7R,MYLK2,MYOT, AHRR FBLN5,NOTCH3 COL4A2 PRKCQ ADRA1D,APOE,CD247,CD3G, IL15,IL1R2,IL7R, AIP FBLN5,NOTCH3 COL4A2 PLIN5,PRKCQ,S100A8 Proteomes 2022, 10, 14 7 of 24 KDM5A HOMER1,MYH4,MYH8,MYL1,TCAP,TNNC2 DNMT3L CASQ1,IFIT1,S100A1,SLIT3,SLN CD79B HOMER1,MMP9,MYH4,MYH8,MYL1, S100A8, EHF NOTCH3 IGF1 TCAP,TFPI,TNC, TNNC2 HDAC4 MYLK2,MYOT SOX15 MMP9, MYOD1 Table 5. Transcription factors (TFs) revealed with upstream network analysis: LR12 + TAK242 treated vs. scrambled peptide and vehicle (30% ethanol)-treated arteries. DEGs with higher expres- sion in LR12 + TAK242-treated group (log2 < −2, p < 0.05) and with higher expression in scrambled peptide-treated group (log2 >2, p < 0.05). Associated with DEGs (log2 < 2 or >−2, p DEGs (log2 <−2, p < 0.05) DEGs (log2 > 2, p < 0.05) Activated Networks < 0.05) ALAS2, ARG1, BCL3, SELP AGT,ALDH1A1,CAT,CEBPA, COL5A1, CXCL8, EGR1,EGR3, ICOS, STAT3 CXCR5, EGR2, IL9R LDHB IL1R1,IL2RB, MYC, SBNO2, NFATC2, PLAGL1,PRDM1 ALDH1A1,ALDH1A2, APOE, ADAM8,ARG1, BCL3, CXCL8, EGR1, ALDH9A1,ASS1,BMX, CAT, EGR3, MYC, DMRT1, TP53 CEBPA, DDIT4L,ECH1, LDHB, ECM1, EDIL3, ITGA2B, ITGB7, NCOR2, EGR2 PPARG, RAMP2, SCD, TCAP, PDE4B, PRDM1, SELP, TMOD4 SERPINB2, VDR AGT,APOE, CEBPA, LDHB, ADAMTS4,BCL3, EGR1, PDE4B, HTT CXCR5, EGR2 MYL1,PPARG COL6A3, PPRC1, PTPN22, RGS14 CD27,CD69, CRTAM,EGR1, IL2RB, MYC, ETS1 HPSE ITGA2B, PRDM1 BCL11B CXCL8 BCL3, CCL22, CCR7,CD69, CXCL8, RELA AGT,APOE, PPARG EGR1, GP1BB, MYC, NFATC2, PDE4B, PRDM1, PTX3,SELP AGT, MYH8,PNPLA2, VDR CXCL8, EGR1, MYC, ITGB7, STAT4,VDR PPARG HDAC4 MYLK2,MYOT SERPINB2 EGR2 RELB CXCL8, MYC, PRDM1, STAT4 MYH4,MYH8,MYL1,TCAP KDM5A TNNC2 ASXL1 PLIN1,PPARG,SCD LEF1 ECM1,MYC, PRDM1 NFKB2 CCR7,CXCL8, MYC CXCR5 NUPR1 MMD ABL2,CXCL8, MYC Associated with inhibited networks BCL3, CCR7,CXCL8, IL1R1, ITGB7, GFI1 CEBPA, EGR2 MYC, STAT4,VDR AGT,APOD,CAT, PPARGC1A LDHB,MYC, PLIN5, PNPLA2, ADAMTS1,IL1R2, COL6A3, OSMR, STC1 SCD,SEMA3G AGT,ASS1,MYH4,MYL1, MYOD1 MYLPF,TNNC2 HLX SEMA3G CXCL8, EGR1, MYC, PRDM1, SPDEF COL5A1, COL6A3,CXCL8 CEBPA, MYH4, MYH8,MYL1, COL5A1, CXCL8, EGR1,EGR3, MYC, RB1 PPARG,RAMP2,TCAP,TNNC2 OSMR, PTX3 IKZF2 CD69,ICOS, IL1R1, LAG3,STAT4 NCOA1 CEBPA, PPARG EGR1, MYC NKX2-3 CAVIN2 CXCL8, RNF213 Proteomes 2022, 10, 14 8 of 24 NEUROG1 ASS1,CAVIN2 KMT2D IGSF1,PPARG Table 6. Transcription factors revealed with causal network analysis: LR12 + TAK242-treated vs. scrambled peptide and vehicle (30% ethanol)-treated arteries. DEGs with higher expression in LR12 + TAK242-treated group (log2 > 2, p < 0.05) and with higher expression in scrambled peptide-treated group (log2 <−2, p < 0.05). Associated with DEGs (log2 < 2 Activated DEGs (log2 >2, p < 0.05) DEGs (log2 <−2, p < 0.05) or >−2, p < 0.05) Networks ADIPOR2, AGT, AQP11, ASS1,BACH1, ABL2,ALAS2,ARG1,BLK,CD27,CD3D, BCAM, BST1,CAVIN2, CEBPA, DDIT4L,ECH1, CHST2,COL5A1,CXCL8,EGR3, DMRT1, EPHX2, LDHB, MDK, MYL1,MYLPF,MYOT, ECM1, FCMR,GP1BA,GP1BB,GPR84, MEF2C PEX11A, PLIN5, PNPLA2, IL1R2,IL2RB,IL7R,ITGA2B,ITGB7,LAG3,LMO2 EGR2, IL9R RAMP1,RAMP2,RETSAT, SCD, SLC9A3R2, ,NFATC2,PLIN1,PLAGL1, PPRC1, TEK, TMOD4, PRDM1,PTPN22,RNF213, SEMA4A,SELP, TNNC2,TSPAN5 ST18,STEAP4, TUBB1,VDR,VWF ADAMTS1,ALAS2, BCL3, CD27, AGT,ALDH1A1,ALDH1A2,AQP11, COL5A1,COL6A3,CXCL8,DMRT1, BCAM,CAT,CAVIN2,CEBPA,CIDEC, EGR1,EGR3,GPR84,HAS1,HPSE,ICOS, DHH, EIF4EBP1,EPHX2, KLHL31, IL1R2,IL1RL1,KDM6B,LAG3,LMO2, CXCR5, EGR2, ISL1 LDHB,LGALS12, MMD,MYH4,MYL1, MYOT, MYC,MMP25,NFATC2,OSMR,PCDH8,PRDM1 IL9R PEX11A, PNPLA2, RAMP1, , PLAGL1,PLIN1,SBNO2, RAMP2,RETSAT,SERPINI1,SFRP5, TCAP, SEMA4A,SERPINB2,ST18,STEAP4, TEK,TNNC2,TSPAN5, WNT11 TCF7,TEAD3,TUBB1 COL5A1,COL6A3,EGR1,FCMR,GP1BA, ALDH1A1,APOE,ASS1,CAT,CAVIN2, HAS1,IL1R1,IL1RL1,IL2RB,ITGA2B, CIDEC,CYP4B1,EPHX2, HCAR1, EHMT2 ITGB7,MUC13,NFATC2,PDE4B, EGR2 LDHB,MMD,MYL1,MYLPF,PEX11A, PRDM1,PLIN1,PTX3,SERPINB2, PLIN5,PPARG, SCD,TCAP TUBB1,VDR,VWF ABL2,ARG1,ADAM8,ADAMTS1, ADAMTS4,BCL3, CCL22,CCR7, CD3D,CD5,CD27,COL6A3, CXCL8, ADRB1,ALDH1A1,ALDH1A2, DUOX2,EGR3, ECM1, EDIL3, GP1BA,GPR84, ALDH9A1,APOD,APOE,BCAM, BMX, HAS1, HPSE, IL1RL1,IL2RB, ITGB7, CAT,CAVIN2,CEBPA,CIDEC,CYP4B1, ITGA11,KDM6B, ASXL1 DDIT4L,ECH1,EPHX2,HCAR1,MYLPF, CXCR5, IL9R LAG3,LMO2,MMP25,MUC13,NAV1, NFATC2, PEX11A,PLIN5,PNPLA2,PPARG, NCOR2, OSM,OSMR, RAMP2,RETSAT,SCD, SEMA3G, PHACTR1,PLAGL1,PLIN1,PRDM1, SERPINI1, TCAP,TMOD4,TNNC2, WNT11 RCAN1,RNF213,SBNO2,SELP, SLAMF7,ST18,STAT4,STC1,STEAP4, TUBB1,VDR ADAM8,ADAMTS4,ALAS2,BCL3, CCL22,CCR7,CD3D,CD27,CD69, AGT, ALDH1A2,ALDH9A1, APOD, COL6A3,CXCL8, DUOX2,ECM1,EGR1, APOE,ASS1,BMX,CAVIN2,CIDEC, FERMT3,GP1BA,GPR84,MYC,EDIL3, CLEC14A,CYP4B1,DDIT4L,ECH1, ICOS,IL1R2,IL2RB,ITGB7,IL7R,KDM6B, CD6, CXCR5, BCL11B EIF4EBP1,ENHO,EPHX2,HCAR1,HPSE,LDHB, LAG3,MUC13,NCOR2,OSM,OSMR, EGR2 MMD,MYLPF,PEX11A,PPARG, PHACTR1,PCDH8,PLAGL1,PLIN5,PRDM1, RAMP1,SERPINI1,TCAP,TEK,TMOD4,TSPAN PPRC1, PTX3, RCAN1, RNF213, 5,WNT11 SBNO2, SEMA4A,SERPINB2,SLAMF7, ST18,STAT4, TCF7,TUBB1,VWF ADAM8, ADAMTS4,ALAS2, BCL3, AGT,ALDH1A2,ALDH9A1,APOD, CCL22,CCR7, CD3D,CD27, CD69,COL6A3, CD6, CXCR5, ANKRD42 APOE,ASS1,BMX,CAVIN2,CIDEC, CXCL8,DUOX2,ECM1, EGR2 CLEC14A,CYP4B1,DDIT4L,ECH1, EGR1, FERMT3,ICOS, EDIL3, GP1BA, Proteomes 2022, 10, 14 9 of 24 EIF4EBP1,ENHO,EPHX2,HCAR1,HPSE,LDHB, GPR84,IL1R2,IL2RB, ITGB7, IL7R, MMD,MYLPF,PEX11A,PPARG, KDM6B,LAG3, MUC13, MYC,NCOR2, RAMP1,SERPINI1,TCAP,TEK,TMOD4, OSM,OSMR, PCDH8, TSPAN5,WNT11 PHACTR1,PLAGL1,PLIN5,PPRC1,PRDM1, PTX3, RCAN1,RNF213,SBNO2, SEMA4A,SERPINB2, SLAMF7,ST18,STAT4, TCF7,TUBB1,VWF ADAM8,ADAMTS4,ALAS2, BCL3, CCL22,CCR7, CD3D, CD27, AGT,ALDH1A2,ALDH9A1,APOD, CD69,COL6A3,CXCL8, DUOX2, APOE,ASS1, BMX,CAVIN2, CIDEC, EGR1, FERMT3,GP1BA,GPR84,ICOS, MYC, CLEC14A, CYP4B1,DDIT4L,ECH1, ECM1, EDIL3, IL1R2,IL2RB, ITGB7, IL7R, CD6, CXCR5, YBX1 EIF4EBP1,ENHO,EPHX2, HCAR1, KDM6B, EGR2 HPSE,LDHB,MMD, MYLPF,PEX11A, LAG3, MUC13, NCOR2, OSM,OSMR, PCDH8, PPARG,RAMP1,SERPINI1,TCAP, PHACTR1,PRDM1, PPRC1, PLAGL1,PLIN5, TEK,TMOD4,TSPAN5,WNT11 PTX3, RCAN1, RNF213,SBNO2, SEMA4A,SERPINB2, SLAMF7,ST18,STAT4, TCF7,TUBB1,VWF ADAMTS4,ALAS2, BCL3, BLK, CCL22,CCR7, CD69, CSF2RB,CXCL8, DMRT1, FERMT3,GP1BA, HAS1, AGT,APOE,ASS1,CAT,CEBPA, ICOS, IL1R1,IL1R2,IL1RL1, IL7R, GATA4 CLEC14A,EIF4EBP1,MYL1,MYLPF, CXCR5 ITGA2B,KDM6B,LMO2, MYC, MYOT,PPARG, RAMP1, TCAP NFATC2,OSM,OSMR,PDE4B, PLAGL1,PRDM1, PTX3, RCAN1,SELP, SERPINB2, ST18, TUBB1,VDR ALAS2, ARG1, CCL22,CCR7,CD5, AGT,APOE,BCAM,CAT,CIDEC, CD27,COL5A1,EGR3, GP1BA,GP1BB, CYP4B1,DHH, HCAR1,MMD,MYH4, HAS1,ICOS, IL2RB, ITGA2B, IL7R, CCND1 MYH8,MYL1,MYLPF,MYOT, PEX11A, LMO2,MYC, MAP3K14,MUC13, NCOR2, CD6, IL9R PLIN5,PNPLA2,PPARG,RAMP2,RBP7, SCD, NFATC2,PCDH8,PLAGL1, TCAP,TNNC2,TSPAN5, WNT11 PLIN1,PPRC1, PTX3,SBNO2,STEAP4, TEAD3,TUBB1,VDR ALAS2, ARG1, BCL3,CCL22,CCR7, CD69,CXCL8, EGR1, FCMR,GP1BA, AGT,APOE, BCAM, CAVIN2,MMD, HPSE,ICOS, IL1R1,IL1RL1,IL2RB, IL7R, MYB EGR2 RCAN1 KDM6B, LMO2,MYC, ITGA2B, PDE4B, PRDM1, PTPN22, PTX3,SERPINB2, ST18,STEAP4,TUBB1,VDR,VWF BCL3, CCL22,CCR7, CD69,CXCL8, EGR1, AGT,ALDH1A1,ALDH1A2,APOE, HAS1,ICOS, IL7R, KDM6B,MYC, ITGA2B, LRRFIP1 EGR2 CEBPA,PPARG, WNT11 PDE4B, PLIN1,PRDM1, PTX3,SERPINB2, ST18,STAT4, TCF7 AGT,ALDH1A1,ALDH1A2,APOD,CAT,CEBP ADAM8,ADAMTS1,CCL22,CCR7, CXCL8, A, EGFL7,LDHB, MYH8,PLIN5, COL6A3, EGR3,IL1R1,IL1R2, IL2RB, MYC, ESRRA PNPLA2,PPARG, SEMA3G,STC1, OSMR, PLIN1,PDE4B, PRDM1, PTX3, STEAP4, WNT11 TCF7 ALAS2, BCL3, CCL22,CCR7,CD27, CD69, AGT,CEBPA,LDHB, MYL1,PPARG, SCD, CXCL8, EGR1, ICOS, IL7R, IL2RB, KDM6B, NOTCH4 TCAP,TEK LMO2, OSM, PRDM1, PTX3,SERPINB2, STAT4, ST18,VWF ALAS2, BCL3, CCL22, CXCL8,CD69, AGT, ALDH1A1, CAT,CAVIN2, DUOX2,FCMR,GP1BA,GPR84, IL7R, GATA1 IL9R LDHB, MMD, PPARG ICOS, IL1R1,IL1R2,IL1RL1, ITGA2B, LMO2, MYC, NFATC2, PHACTR1, PLAGL1,PDE4B, Proteomes 2022, 10, 14 10 of 24 PTPN22,RNF213, SBNO2, STAT4, ST18,TUBB1,VDR,VWF BCL3, BLK,CCL22,CCR7,IL7R,KDM6B, AGT,APOE,ASS1, CD69,CXCL8, DMRT1,EGR1,ICOS, MYC, TBX5 EGR2 MYL1,MYLPF,MYOT, PPARG NFATC2, PLAGL1,PTX3, SERPINB2, ST18,STAT4 AGT,APOD, BCAM,CAT, CYP4B1, ADAMTS1,ARG1,COL6A3, IL7R,CXCL8, NRIP1 LDHB,PLIN5,PNPLA2,PPARG, EGR1, IL1R1,IL2RB, OSMR, PLIN1, PTX3, EGR2 SCD,SEMA3G,STC1 STEAP4 ARG1, BCL3, CCL22,CCR7,CD69,EGR1, TRIM32 AGT,APOE, CEBPA,PPARG ICOS, IL7R,KDM6B,PRDM1, PTX3, SELP, CXCR5 SERPINB2, STAT4, ST18 BCL11B CXCL8, IL7R HDAC4 MYLK2,MYOT EGR2 Associated with inhibited networks AGT,ALDH1A1,ALDH1A2,APOE, ALAS2, ARG1, BLK, CD3D,CD27, ASS1,CASQ1,CAT, CEBPA,CIDEC, CHST2,COL5A1, CSF2RB, DUOX2, CYP4B1,DDIT4L,ECH1, EIF4EBP1, EGR3, FCMR,GP1BA,GP1BB,IL1R2, IL1RL1, EPHX2,HCAR1,LDHB, MDK,MMD, DMRT1, ITGA2B, KDM6B, LMO2, MUC13, HDAC5 CD6, EGR2, IL9R MYC,MYH4,MYH8,MYL1,MYLPF, NFATC2,OSM,OSMR, PHACTR1, MYOT,PEX11A, PLIN5,PNPLA2, PLAGL1,PLIN1,PTPN22, PPARG, RAMP2, RETSAT,SCD, SERPINI1, RCAN1,RNF213,SBNO2,SELP, TEK,TNNC2 STC1,ST18,STAT4,TUBB1,VDR ABL2,ADAMTS1,ADAM8,ADAMTS4,ARG1,B ADRB1,ALDH1A1,ALDH1A2, CL3,CCL22,CCR7,CD3D,CD5, ALDH9A1,APOD,APOE,BCAM, CD27,COL6A3, DUOX2,EGR3, ECM1, EDIL3, BMX,CAT,CAVIN2,CEBPA,CIDEC, GP1BA,GPR84, HAS1, HPSE, IL1R2,IL1RL1, CYP4B1,DDIT4L,ECH1, EPHX2, ITGA11,ITGB7,KDM6B, LAG3,LMO2, SREBF1 CXCR5, IL9R HCAR1,MDK,MYL1,MYLPF,PEX11A, MMP25,MUC13,MYC, PLIN5,PNPLA2,PPARG, RAMP2, NAV1,NFATC2,OSM,OSMR,PHACTR1,NCOR RETSAT,SCD, SEMA3G,SERPINI1, 2, PLAGL1,PLIN1,PRDM1, TCAP,TMOD4,TNNC2,WNT11 RCAN1,RNF213,SBNO2,SELP,SLAMF7, STC1,ST18,STAT4, TUBB1,VDR ABL2,ADAM8,ADAMTS4, ADIPOR2, ARG1, BCL3,CCL22,CCR7,CD3D, CD5, ADRB1,AGT,ALDH1A2,ALDH9A1, CD27,CXCL8,DUOX2,EGR1,EGR3, HAS1, APOD,APOE,BMX,CAT,CAVIN2, HPSE, IL1RL1, ECM1, EDIL3, GP1BA,GPR84, CEBPA,CIDEC, CYP4B1,DDIT4L,DHH, ITGB7, ITGA11, ECH1, EPHX2,HCAR1,MYH4,MYH8, MED24 KDM6B,LAG3,LMO2,MAP3K14, MUC13, CXCR5, IL9R MYLPF,MYOT,PEX11A,PLIN5, MYC, MMP25,NCOR2, NFATC2, PNPLA2,PPARG, RAMP2,RETSAT, NAV1,OSM,PHACTR1, SCD,SEMA3G, SERPINI1,SGK2, PLAGL1,PLIN1,PRDM1,RCAN1,RNF213,SBN STC1,TCAP, TMOD4,TNNC2,WNT11 O2, SEMA4A,SELP, ST18,STAT4, SLAMF7,TEAD3, TUBB1,VDR ABL2, ADAMTS1,ADAMTS4,ADAM8, ARG1, BCL3, CCL22,CCR7, CD3D,CD5, ADRB1,ALDH1A1,ALDH1A2, CD27,COL6A3,CXCL8, DUOX2,EGR3, ALDH9A1,APOD,APOE,BCAM, BMX, EGR1,GP1BA,GPR84,IL1RL1,IL2RB, CAT,CAVIN2,CEBPA,CIDEC,CYP4B1,DDIT4L ECM1,EDIL3,HAS1, HPSE,ITGB7, Ncoa6 ,ECH1,EPHX2,HCAR1,MYLPF, CXCR5, IL9R ITGA11,KDM6B,LAG3,LMO2,MMP25,MUC13, PEX11A,PLIN5,PNPLA2, RAMP2, NAV1,NCOR2,NFATC2, OSM, RETSAT, SCD, SEMA3G,SERPINI1, OSMR,PHACTR1,PLAGL1,PLIN1, PRDM1, TCAP,TMOD4,TNNC2, WNT11 RCAN1,RNF213,SBNO2,SELP, SLAMF7, STC1,ST18, STEAP4,STAT4, TUBB1, VDR Proteomes 2022, 10, 14 11 of 24 ADAM8,ADAMTS4,ALAS2, BCL3, CCL22,CCR7, CD3D,CD27,CD69, COL6A3, AGT, ALDH1A2,ALDH9A1,APOD, CSF2RB,CXCL8, DUOX2, ECM1, EDIL3, APOE,ASS1, BMX,CAVIN2,CIDEC, FERMT3,GP1BA,GPR84, HPSE,ITGB7,ICOS, CLEC14A,CYP4B1,DDIT4L, ECH1, IL1R2,IL2RB, IL7R, CD6, CXCR5, ZBTB32 EIF4EBP1,ENHO,EPHX2,HCAR1, KDM6B,LAG3,MYC, MUC13,NCOR2, EGR2 LDHB,MMD,MYLPF, PEX11A,PPARG, OSM,OSMR,PHACTR1,PCDH8,PLAGL1,PLIN RAMP1,SERPINI1,TCAP,TEK,TMOD4,TSPAN 5,PPRC1,PRDM1, PTX3,RCAN1, 5,WNT11 RNF213,SBNO2, SEMA4A, SERPINB2, SLAMF7,ST18,STAT4, TCF7,TUBB1, VWF ADAM8,ALAS2, CD3D,COL5A1, AGT, ALDH1A2,APOE,ASS1,BACH1, COL6A3, CSF2RB,EGR3, FCMR, HAS1, ICOS, CAT,CAVIN2, CEBPA,DHH, EPHX2, IL1R2,IL1RL1,IL2RB, IL7R,LAG3, MYC, NKX2-1 LDHB,LGALS12,MDK,MMD,MYH4, ITGA2B, MMP25, NAV1,NCOR2, NFATC2, CD6, CXCR5, IL9R MYH8,MYL1,MYLPF,PEX11A, RAMP1, OSM,OSMR, PLAGL1,PLIN1, PRDM1, PTX3, SCD, TCAP,WNT11 RCAN1,SBNO2,SELP, SERPINB2, TCF7,TUBB1, VDR,VWF ADRB1,AGT,ALDH1A1,ALDH1A2, ARG1, BCL3, CCL22,CCR7,COL5A1, ALDH9A1,APOD,ASS1,BACH1,BCAM, EGR1,EGR3, HAS1, HPSE,IL1R1,IL2RB, ECM1, BMX,CAVIN2,CIDEC,CYP4B1,DDIT4L,DHH,E EDIL3, FCMR,GP1BA,ITGB7, CXCR5, EGR2, NEUROG2 CH1,EIF4EBP1,EPHX2, HCAR1, KDM6B,MMP25,MUC13,NFATC2,OSM,OSMR IL9R LDHB,MDK,MMD,MYH4,MYL1, , PLAGL1,PLIN1,RCAN1, MYLPF,PLIN5,RAMP2,TCAP,TEK, SBNO2,ST18,STAT4,STEAP4, TCF7, TMOD4, WNT11 TUBB1,VWF ADAM8,ARG1, BCL3, CD69, CD3D, CSF2RB,CXCL8,DUOX2, EGR1,EGR3, FCMR, GPR84, HSH2D,IL1R2,IL1RL1, ALDH1A2,APOE,AQP11, ASS1, BACH1, BST1, IL2RB, ITGB7, KDM6B,LAG3,MYC, DLX2 CEBPA,EIF4EBP1, EGR2 NFATC2,OSM, MMP25,NAV1, ENHO,FFAR4,MMD,TSPAN5, WNT11 PHACTR1,PRDM1,PTX3,RNF213,SBNO2,SELP . SEMA4A, SLAMF7, SERPINB2, ST18,STAT4, TCF7,VDR ADAMTS1, ADAMTS4, ADIPOR2, CD27, ADRB1,AGT,APOD,APOE,AQP11,ASS1,CAVI CD69, CD3D,CD5,COL5A1, N2,CEBPA,DNAH11, CSF2RB,COL6A3,CXCL8, EGR1, EIF4EBP1,ENHO,EPHX2,HCAR1,MMD,MYH4 FCMR,GP1BA, HAS1,ICOS,IL1R2, IL1RL1, NCOA6 IL9R ,MYLPF, PEX11A,PON3, PPARG,RETSAT, ITGA2B, KDM6B, LMO2, MAP3K14,MYC, S100A1, SEMA3B, OSM,PHACTR1, PCDH8,PLAGL1,PLIN1, SERPINI1,SLC9A3R2,TNNC2, WNT11 PTX3, RELT,RNF213, SBNO2,SEMA4A, STAT4, STC1,TCF7,TUBB1, VWF,VDR ADAM8,ARG1, BCL3, CD3D,CD69, CSF2RB,CXCL8, DUOX2,EGR1,EGR3, FCMR, ALDH1A2,APOE,AQP11, ASS1, GPR84, HSH2D,IL1R2,IL1RL1, FOXJ1 BACH1,BST1,CEBPA,EIF4EBP1,ENHO, IL2RB, ITGB7, KDM6B,LAG3, MMP25, EGR2 FFAR4,MMD,TSPAN5, WNT11 NAV1,NFATC2,OSM,PHACTR1,PRDM1, PTX3,RNF213,SBNO2, SELP, SEMA4A, SLAMF7,SERPINB2, ST18, STAT4, TCF7,VDR ADAM8,ARG1, BCL3, CD3D,CD69, CSF2RB,CXCL8,DUOX2, EGR1,EGR3, FCMR, GPR84,HSH2D,ITGB7,IL1R2, IL1RL1,IL2RB, ALDH1A2,APOE,AQP11,ASS1,BACH1, KDM6B,LAG3,MYC, MMP25,NAV1, PRDM1, FOXD1 BST1,CEBPA,ENHO,FFAR4,MMD, EGR2 PTX3, TSPAN5, WNT11 NFATC2,OSM,PHACTR1,RNF213,SBNO2, SELP, SERPINB2, SEMA4A, SLAMF7,ST18,STAT4, TCF7,VDR Proteomes 2022, 10, 14 12 of 24 ADRB1,AGT, ALDH1A1,ALDH9A1, ALAS2, BCL3, COL5A1, CSF2RB, CXCL8, APOD,ASS1,BACH1,BCAM, BMX, DUOX2,EGR3,FCMR,GP1BA, CIDEC, CYP4B1,DDIT4L,DHH, ECH1, IL1R1,IL1RL1,IL2RB, ECM1, EDIL3, ITGB7, ARHGAP35 EIF4EBP1, HCAR1,KDM6B,LDHB, CXCR5, IL9R MUC13,NFATC2,OSM,OSMR, MMD,MYH4,MYLPF,PEX11A, PLIN5, PLAGL1,PLIN1, PTX3,RCAN1,SBNO2, RAMP1,RAMP2, SCD,TCAP,TEK, STEAP4, ST18,STAT4, TUBB1,VWF TMOD4,TNNC2 ADRB1,ALDH1A2,APOE,CAT,CAVIN2,CEBP ARG1, CCL22,CCR7, CD69,COL5A1, A,CIDEC,CYP4B1, HCAR1, MED1 COL6A3,EGR1,IL1R2,IL1RL1,IL2RB, MYC, MUC13, PEX11A,PLIN5,PNPLA2, PPARG, PDE4B, PLIN1,PRDM1, STC1,VDR SCD,SEMA3B ALDH1A2,APOE, CAT,CAVIN2, ARG1, CCL22,CCR7,CXCL8, Ncoa6 CEBPA,CIDEC, CYP4B1,HCAR1, PLIN1,PRDM1, VDR MUC13,PEX11A, PLIN5, SCD ALDH1A2,APOE, CAT,CAVIN2, ARG1, CCL22, CEBPA,CIDEC, CYP4B1,HCAR1, ZNF423 CCR7,CXCL8, MYC, MUC13, PEX11A, PLIN5,PPARG, PLIN1,PRDM1, VDR SCD ALAS2, ARG1, BCL3, CCL22,CCR7, AGT, ALDH1A2,APOE, CAT,CAVIN2, CD69,CXCL8, EGR1,EGR3,ICOS, IL7R, CITED2 CIDEC,CYP4B1, HCAR1,PEX11A, KDM6B,MUC13, MYC, PLIN1,PTX3, PLIN5,PPARG, SCD,SEMA3B,TEK SERPINB2, ST18,STAT4,VDR ALAS2, BCL3, CD69, CXCL8, CCL22, DUOX2, FCMR,GP1BA,GPR84, IL7R, ICOS, IL1R1,IL1R2,IL1RL1, ITGA2B, AGT, ALDH1A1, CAT,CAVIN2, CBFA2T3 LMO2,MYC,NFATC2,PHACTR1, IL9R LDHB, MMD,PPARG PLAGL1,PDE4B, PTPN22,RCAN1, RNF213,SBNO2, STAT4,ST18,TUBB1, VDR, VWF CD3D,CD5, CXCL8, ICOS,IL1RL1, IL2RB, AGT,ASS1, EIF4EBP1,MYL1,MYLPF, TCF12 ITGB7, IL7R,LMO2, IL9R PPARG,TNNC2 PRDM1, SEMA4A, SLAMF7 APOE, ASS1,CAT,CEBPA,CIDEC, ECH1, ARG1, LMO2,MYC, PLIN1, HLF MMD, PEX11A,PLIN5, PNPLA2, PPARG,RETSAT,SCD IKZF2 CD69,ICOS, IL1R1, LAG3,STAT4 MYOD1 AGT,ASS1,MYL1,MYLPF,TNNC2 GFI1 CEBPA BCL3, CCR7,IL1R1, IL7R,ITGB7, STAT4 BCL3, CXCL8, EGR1, KDM6B, HLX RAMP1,RAMP2,SEMA3G,TSPAN5 MYC, PRDM1 SPDEF COL5A1, COL6A3,CXCL8 STRING network analysis showed the interaction of various proteins with each other. STRING network analysis for genes (encoded by genes being regulated by TFs in this study) showed protein–protein interactions among each other. Specifically, ARNT2 showed interaction with HIF-1α; BCL11B with SIRT2, HDAC1, and HDAC2; CITED2 with HIF-1α, ARNT, FOXO1, and CREBBP; ETS1 with FOXO1, JUN, MAPK1, and FOS; MED1 with PPARG and ESR1; MYOD1 with HDAC4, PPARGC1A, HDAC5, MEF2C, and GATA4; NFKB2 with NFKB1, RELA, and RELB; SPI1 with GATA1, CEBPA, and JUN; and STAT3 with JAK1, JAK2, HIF-1α, IL10RA, and HSP90AA1 (Figure 1). Similarly, CCL5 in- teracted with CCR2, CCR3, CCR5, CCR1, IL6, IL10, IL1B, and TNF; RSRRA with PPARGC1A, HIF-1α, and MEF2C; GFI1 with SPI1; HDAC3 with RELA, PPARG, and HDAC4; HDAC5 with MEF2A; NEUROG2 with ISL1; and VDR with MED1 (Figure 2). Proteomes 2022, 10, 14 13 of 24 The analysis showed protein–protein interaction by the proteins involved in vascular pa- thologies, immune cell responses, inflammation, and hypoxia and this suggests their probable role in AVF maturation and maturation failure. ARHGAP3 ARNT MED1 STAT BCL11B ISL1 CCND ETS1 SPI1 Proteomes 2022, 10, 14 14 of 24 MEF2C CITED NFKB Figure 1. STRING network analysis for protein–protein interactions for Rho GTPase-activating pro- tein 35 (ARHGAP35), aryl hydrocarbon receptor nuclear translocator 2 (ARNT2), mediator of RNA polymerase II transcription subunit 1 (MED1), signal transducer and activator of transcription 3 (STAT3), B-cell lymphoma/leukemia 11B (BCL11B), insulin gene enhancer protein ISL-1 (ISL1), G1/S-specific cyclin-D1 (CCND1), transcription factor PU.1 (SPI1), myocyte-specific enhancer factor 2C (MEF2C), Cbp/p300-interacting transactivator 2 (CITED2), and nuclear factor NF-kappa-B p100 subunit (NFKB2). HDAC PPARGC1A GFI1 ESRRA HDAC MYB Proteomes 2022, 10, 14 15 of 24 MYOD1 NEUROG SMARCC1 LRRFIP CCL5 VDR Figure 2. STRING network analysis of protein–protein interaction for peroxisome proliferator-acti- vated receptor gamma coactivator 1-alpha (PPARGC1A); histone deacetylase 3 (HDAC3); zinc fin- ger protein Gfi-1 (GFI1); steroid hormone receptor ERR1 (ESRRA); histone deacetylase 5 (HDAC5); transcriptional activator Myb (MYB); myoblast determination protein 1 (MYOD1); neurogenin-2 (NEUROG2); Swi/snf-related, matrix-associated, actin-dependent regulator of chromatin subfamily c member 1 (SMARCC1); leucine-rich repeat flightless-interacting protein 1 (LRRFIP1); C-C motif chemokine ligand 5 (CCL5); and vitamin D receptor (VDR). 4. Discussion Intimal injury during AVF creation induces NIH, which helps in vascular remodeling and makes vessel suitable for increased blood flow through the outflow vein. However, excessive NIH and early thrombosis of the inflow artery or excessive NIH in the outflow vein can lead to vessel stenosis and AVF maturation failure [12]. Neointimal reendotheli- alization, with the aim of obtaining an intact endothelium, is necessary for the proper healing and AVF maturation and involves endothelial cell (EC) proliferation and migra- tion and the formation of tight adherence junctions. Small GTPases regulate these pro- cesses, and their dysregulation may cause vascular pathologies, including atherosclerosis (plaque formation) and angiogenesis. Rho GTPases (Rho, Rac, and Cdc42) play a critical role in vascular pathologies and become activated when they bind to GTP and are inacti- vated when they bind to GDP. RhoGTPase also plays a role in regulating molecular pro- cesses such as contraction, migration, proliferation, and the differentiation of smooth mus- cle cells and fibroblasts [20–22]. This suggests the critical role of RhoGTPase in vessel re- modeling, involving the migration and proliferation of ECs and VSMCs. These molecular processes are regulated by various genes and increased or decreased gene expression may alter the migration and proliferation of ECs and VSMCs. Gene expression is regulated by Proteomes 2022, 10, 14 16 of 24 TFs [13] and the inhibition of transcription factor Rho GTPase-activating protein 35 (ARHGAP35), as demonstrated in this study by thrombosed artery RNA seq data, in as- sociation with various DEGs involved in VSMC and EC proliferation and migration, an- giogenesis, and inflammation (Table 6), suggesting the role of ARHGAP35 in early throm- bosis and thereby in AVF maturation failure. However, the underlying mechanistic as- pects warrant further investigation. Hypoxia is another critical factor regulating NIH, EC, and VSMC migration and pro- liferation, extracellular matrix (ECM) remodeling, and vessel remodeling [23]. Hypoxia is caused due to disruption of the vasa vasorum during the creation of AVF and this induces transcription factors including hypoxia-inducible factors (HIFs) such as HIF-1α, HIF-2α, HIF-3α, HIF-1β (aryl hydrocarbon receptor nuclear translocator; ARNT), and HIF-2β (ARNT2) [23]. In this study, we observed activated ARNT2 in the thrombosed arteries (Table 3) with ApoE, a gene regulating vasodilatation and anti-remodeling, as a target DEG in the data set. This suggests that ARNT2 plays a critical role in AVF non-maturation. Aryl hydrocarbon receptors also play a critical role in ischemia-induced angiogenesis [24] and angiogenesis plays a critical role in plaque vulnerability [25], thrombosis [26], and short expectancy of AVF [27]. Activated ARNT2 in this study might be a therapeutic target to facilitate AVF maturation, as aryl hydrocarbons have been suggested as a therapeutic target in cardiovascular diseases [28,29]. Excessive intimal hyperplasia is also a result of increased ROS production and inflammation, mediated by macrophages that are deficient in MED1 [30]. Inhibited MED1 (Table 6) and the infiltration of pro-inflammatory immune cells, including macrophages, in the thrombosed and intimal hyperplasia region of femo- ral arteries in this study support the notion of the presence of inflammation [12] and a the possible role of MED1-deficient macrophages in early thrombosis and AVF failure. In- flammation within the evolving plaque is associated with the activation of signal trans- ducers and activators of transcription (STAT)1 and STAT3, and STATs function as intra- cellular regulators of vascular remodeling [31]. STAT3 is also involved in collagen-in- duced platelet aggregation and thrombosis [32] and was found to be activated in this study, indicating its role in early arterial thrombosis. BCL11B (B-cell leukemia 11b) is another transcription factor that was found to be elevated (Table 5) in the upstream network analysis of our data. BCL11B plays a crucial role in the development, proliferation, differentiation, and survival of T cells. Valisno et al. reported the expression of BCL11B in VSMCs and its role in aortic smooth muscle func- tion and vascular stiffness [33]. Since vascular stiffness adversely affects AVF maturation, increased expression of BCL11B in thrombosed arteries suggests its pathological role and its therapeutic potential as a target. Vascular stiffness, mainly arterial stiffness, plays a critical role in hypertension, involving a change in the gene expression and phenotype of VSMCs and ECs and their focal contacts. In ECs and VSMCs, Lin11-Isl1-Mec3 (LIM) do- main-containing proteins play a critical role as mechanotransducers [34] and hemody- namic changes occur after AVF creation and contribute to AVF maturation and/or failure. Investigating the role of Isl1, which was found to be activated in this study, will be of significance. Differentiation and proliferation of VSMCs and dysfunction of ECs play a crucial role in stenosis, thrombosis, and remodeling and determine the fate of AVFs. c- Myb, a TF that was found to be activated in this study, regulates VSMC progenitor cells, VSMC differentiation, VSMC proliferation, and arterial remodeling [35,36]. Since early arterial thrombosis, stenosis, and adverse remodeling are associated with early AVF fail- ure in the presence of chronic inflammation [12], considering the mediators regulating arterial remodeling, along with outflow vein remodeling, will be of significance in en- hancing vessel and AVF patency. VSMC proliferation plays a crucial role in the pathogenesis of plaque formation and NIH and cyclin D1 play a critical role [37]. Furthermore, the attenuation of adverse vas- cular remodeling via CCND1 inhibition supports the therapeutic potential of targeting CCND1 [38]. The increased expression of CCND1 observed in thrombosed vessels (Table Proteomes 2022, 10, 14 17 of 24 6) in this study indicates that CCND1 might be a therapeutic target to enhance AVF mat- uration by attenuating NIH and vessel stenosis. The ETS family of transcription factors plays a critical role in plaque formation and vulnerability and increased expression of Ets- 1 in VSMCs is associated with plaque vulnerability [39], vascular inflammation, and re- modeling [40]. An increased expression of ETS and SPI1 (the transcription factor PU.1) in the thrombosed arteries in this study suggest their role in early thrombosis, mediating early AVF failure. VSMC proliferation and NIH are also regulated by GATA4-mediated cyclin D1 transcription [41] and we observed activated GATA4 in our study (Table 6). Interferon regulatory factor (IRF) 1, which was also found to be activated in this study, is another TF regulating neointima formation after intimal injury and preventing NIH, in- volving the Ang-II receptor and attenuating vascular remodeling [42]. On the other hand, IRF9, a transcription factor that was found to be activated in this study, is essential for neointima formation and VSMC proliferation after vascular injury [43]. EC proliferation is regulated by another TF, namely, MEF2 (myocyte enhancer factor 2) and we observed activated MEF2C (Table 6) in our study. MEF2 is an upstream regulator of several tran- scription factors and it promotes an anti-inflammatory and antithrombotic endothelium by regulating TFs, including Klf2, Klf4, and Notch [44]. This suggests the critical role of MEF2C in vessel thrombosis and it might be a therapeutic target for enhancing vessel pa- tency and AVF maturation. This notion is supported by the activated NOTCH4 observed in this study. CITED2 (CBP/p300-interacting transactivator with ED-rich tail 2) suppresses genes mediating angiogenesis and ECM remodeling and regulates the expression of various MMPs [45] and HIF-1α [46] and regulates inflammatory genes involved in fibrosis [47,48]. HIF-1 α, angiogenesis, inflammation, and ECM remodeling play a critical role in AVF maturation and failure, and the regulation of CITED2 expression may play a therapeutic role. The decreased expression of CITED2 observed in this study suggests its probable role in thrombosis and stenosis; however, its role in AVF maturation and failure warrant investigation. ECM remodeling is mediated by various MMPs and the activation of NF- κB regulates the expression of MMPs. The change in hemodynamics associated with shear stress activates NF-κB and regulates long-term flow-induced vascular remodeling [49]. The activated NF-κB2 (nuclear factor Kappa B subunit 2), REL-A (RELA proto-oncogene, NF-κB subunit), and REL-B (RELB proto-oncogene, NF-κB subunit) observed in this study suggest the role of NF-κB in inflammation [12], stenosis, and remodeling, and targeting NF-κB might have therapeutic potential. Network analysis with the transcription factors as inputs revealed molecular mechanisms, including macrophage polarization, the in- volvement of reactive oxygen species, fibrosis, cell cycle, apoptosis, mitochondrial biogen- esis, adipogenesis, and lipogenesis, as well as the interaction of various transcription fac- tors with DEGs (Figure 3). Network analysis also revealed the involvement of FOXO genes and this finding is important because FOXO4 plays a critical role in early inflam- matory responses after intimal injury and in apoptosis [50,51]. Proteomes 2022, 10, 14 18 of 24 Figure 3. Network analysis with TFs as inputs revealed molecular mechanisms associated with in- flammation, hypoxia, adipogenesis, lipogenesis, and mitochondrial biogenesis associated with early thrombosis. Blue circles—input transcription factors, red circles—differentially expressed genes, and yellow squares—molecular mechanisms associated with early thrombosis in arteries involved in arteriovenous fistula. NIH is a major contributor of arterial restenosis. Vitamin D, exerting its biological effect through the vitamin D receptor (VDR), plays a critical role in mitigating inflamma- tion and excessive NIH [52–54]. VDR was inhibited in this study, and this might be due to the presence of chronic inflammation [12]. Decreased expression of VDR is correlated with arterial thrombosis and as per previous reports. The CC chemokine CCL5/RANTES (regulated on activation normal T cell expressed and secreted), which is expressed in VSMCs, regulates NIH, and CCL5 expression in smooth muscle cells is regulated by tran- scription factor YBX-1, which was found to be activated in this study, coding for Y-box binding protein-1 [55]. Chen et al. [56] reported the role of EHMT2 (also known as G9a) in the regulation of aortic smooth muscle cell death by suppressing autophagy activation independently of proliferation and apoptosis. Histone lysine methyltransferase EHMT2 also plays a role in Proteomes 2022, 10, 14 19 of 24 vasculopathy and vascular inflammation [57]. Another TF, GATA1, regulates EC function and angiogenesis by regulating AGGF1 [58]. Increased expression of HDAC4 is associated with vascular inflammation and associated inflammatory diseases via the activation of autophagy, and knockdown of HDAC4 ameliorates vascular inflammation [59]. We ob- served HDAC4 to be inhibited in the LR12 + TAK-242-treated arteries and activated in scrambled and ethanol-treated arteries in this study, and this might be the effect of the treatment given to the swine. Similarly, the inhibition of HDAC5 is associated with the reduction of angiotensin II-induced vascular contraction, hypertrophy, and oxidative stress in a mouse model [60], and HDAC5 was found to be inhibited (Table 6) in this study, as an effect of the treatment given. Investigating the role of epigenetic regulators including HDACs and H3K9me3 is of significance because they have abnormal mechanoresponses to arterial laminar shear stress and play a role in EC damage [61], with etiologies playing a critical role in vessel remodeling in AVF. miRNAs regulate gene expression epigenetically and miR-132 upregulation is asso- ciated with reduced VSMC proliferation by attenuating the expression of LRRFIP1 and thus attenuating neointima formation [62]. The transcription factor LRRFIP1 was acti- vated (Table 6), and miR-132 appeared in the data (Supplementary File S1) but was neither activated nor inhibited. The activation of LRRFIP1, a target of miR-132, in thrombosed and stenosed arteries in this study, is indicative of excessive VSMC proliferation. An at- tenuated expression of mir-133 is associated with the phenotypic switching, proliferation, and migration of VSMCs and thereby plays a role in vascular remodeling [63]. miR-155, by downregulating the soluble guanylyl cyclase (sGC/cGMP) pathway, negatively regu- lates VSMC functions that are essential for maintaining the VSMC contractile phenotype and vasorelaxation [64]. miR155-5p, which was found to be inhibited in this study, sup- presses ACE expression and its downstream production of Ang II, superoxide anions, and inflammatory factors, and it inhibits VSMC migration and oxidative stress in spontane- ously hypertensive rats but has no effects on VSMC migration with exogenous Ang II [65]. miR155-5p in adventitial fibroblast-derived extracellular vesicles inhibits the proliferation of VSMCs [66]. Since oxidative stress, hypoxia, and VSMC proliferation play a critical role in AVF maturation/failure, these miRNAs might play an important role. The levels of miR- 34a-5p, associated with atherosclerosis and miR-34a-5p, play a crucial role in inflamma- tory pathology [67] and various studies [68,69] have discussed miR-34a-5p, which was found to be inhibited in this study, as a potential therapeutic target in cardiovascular dis- ease. miR-205-5p, which was found to be activated in this study, suppresses the prolifer- ation of pulmonary VSMCs [70], whereas miR-153, which was found to be inhibited in this study, attenuates hypoxia-induced excessive proliferation and migration of pulmo- nary arterial SMCs [71]. miR-22, which was found to be inhibited in this study, regulates VSMCs apoptosis (through p38MAPKα) and vascular remodeling in aortic dissection [72], whereas miR-219, which was found to be inhibited in this study, is associated with vascu- lar ischemia during cerebral artery occlusion [73] (Tables 1 and 2). The expression of these miRNAs in this study and their relationship with vascular pathologies suggest that these miRNAs may be potential therapeutic targets in AVF. Other miRNAs which have been described in relation to vascular remodeling, including miR-1 and miR-204 [74], also ap- peared in this study but were neither activated nor inhibited. Additionally, this study re- vealed some novel miRNAs which have not been discussed in the literature (Supplemen- tary File S1) in relation to arterial stenosis and thrombosis involved in AVF and which may cause early AVF maturation failure and thus might have therapeutic potential, since microRNAs play a critical role in AV shunting, angiogenesis, thrombosis, restenosis, NIH, and vessel remodeling [75–78]. 5. Future Perspectives Overall, the results of this study revealed various TFs and microRNAs associated with early arterial thrombosis, stenosis, and nonpatent AVF. The role of various TFs and Proteomes 2022, 10, 14 20 of 24 microRNAs has been discussed in the literature in association with inflammation; the pro- liferation and migration of VSMCs; the proliferation and dysfunction of ECs; ECM remod- eling; and angiogenesis in terms of vascular remodeling, as discussed above. However, the roles of these TFs and microRNAs and others that appeared in this study have not been investigated in relation to AVF maturation and maturation failure. The involvement of other TFs and microRNAs found in this study has been discussed in relation to embry- onic organogenesis, vasculogenesis, cardiac remodeling, etc. (Supplementary File S3) this but warrants further research in association with AVF. For example, NRIP1, which was found to be activated in this study, also appeared in another data set [79] in association with blood coagulation, ECM remodeling, inflammatory responses, the TGF signaling pathway, and angiogenic proteins, and all these play a role in AVF maturation and failure. Similarly, TF NUPR1 encoding nuclear protein 1 (Nupr1), a marker for phenotypic mod- ulation (modSMCs) towards a fibroblast-like state, which was found to be activated in this study, also appeared in another single-cell transcriptomic profile of VSMC phenotypes [80]. PPARGC1A, a TF that was observed to be inhibited in this study, also appeared in another genomic and transcriptomic analysis highlighting the role of vascular changes in multiple sclerosis—mainly immune and inflammation-related pathologies mediating perivascular changes [81]. Since perivascular cuffing and perivascular inflammation are associated with arterial thrombosis [12], investigating the role of PPARGC1A will enhance the understanding of the molecular mechanism underlying early AVF failure. SMARCC1 (Swi/snf-related, matrix-associated, actin-dependent regulator of chromatin subfamily c member 1) and SMARCA4 (Swi/snf-related, matrix-associated, actin-dependent regulator of chromatin, subfamily a, member 4) was found to be activated in this study, and Brg1, a central catalytic subunit of the SWI/SNF apparatus, plays a critical role epigenetically in VSMC proliferation [82]. ZNF423 plays a role in activating autophagy by binding to BCAT1 in hypoxic pulmonary artery smooth muscle cells [83], since hypoxia occurs after AVF creation due to disruption of the vasa vasorum. Therefore, ZNF423 might play an important role in AVF maturation and maturation failure. Furthermore, the protein–pro- tein interactions between the genes regulated by these TFs support the notion of their role in early thrombosis, and targeting them might be of therapeutic significance in AVF. 6. Conclusions The results of this study revealed various transcription factors and microRNAs, of which some have implications in vascular remodeling, NIH, VSMC proliferation and mi- gration, and EC proliferation and function (as discussed) and some have been discussed in the literature in other contexts (Supplementary File S3). However, none of these TFs and microRNAs have been investigated in relation to early thrombosis, stenosis, vessel patency, AVF maturation, and maturation failure after AVF creation and thus warrant further research. Since genetic and epigenetic regulation of genes is an evolving research area, investigating genetic and epigenetic mediators playing a critical role in AVF matu- ration failure and targeting them will be of great significance to improving clinical out- comes. Supplementary Materials: The following supporting information can be downloaded at: www.mdpi.com/article/10.3390/proteomes10020014/s1, Supplementary File S1: List of all mi- croRNAs; Supplementary File S2: List of all transcription factors; Supplementary File S3: Details of transcription factors. Author Contributions: Conceptualization, V.R.; methodology, V.R.; software, V.R.; validation, V.R. and D.K.A.; formal analysis, V.R.; investigation, V.R.; resources, D.K.A.; data curation, V.R.; writ- ing—original draft preparation, V.R.; writing—review and editing, D.K.A.; visualization, V.R.; su- pervision, D.K.A.; project administration, D.K.A.; funding acquisition, D.K.A. All authors have read and agreed to the published version of the manuscript. Funding: V.R. is supported by an intramural grant IMR Rai 12397B from the Western University of Health Sciences, Pomona, California. The research work of D.K.A. is supported by the R01 Proteomes 2022, 10, 14 21 of 24 HL144125 and R01 HL147662 grants from the National Institutes of Health, USA. The content of this critical review is solely the responsibility of the authors and does not necessarily represent the offi- cial views of the National Institutes of Health. Institutional Review Board Statement: The Institutional Animal Care and Use Committee (IACUC) at Western University of Health Sciences approved protocol No. R20IACUC038 for this study. Informed Consent Statement: Not applicable. Data Availability Statement: All data supporting the results of this manuscript have been included in this manuscript along with Supplementary Files. Bulk RNA seq (Fastq. 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Proteomes – Multidisciplinary Digital Publishing Institute

Published: Apr 30, 2022

Keywords: arteriovenous fistula; early thrombosis; epigenetic regulation; maturation failure; microRNAs; transcription factors; transcriptional regulation

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Transcriptional and Epigenetic Factors Associated with Early Thrombosis of Femoral Artery Involved in Arteriovenous Fistula

Transcriptional and Epigenetic Factors Associated with Early Thrombosis of Femoral Artery Involved in Arteriovenous Fistula

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Transcriptional and Epigenetic Factors Associated with Early Thrombosis of Femoral Artery Involved in Arteriovenous Fistula

Transcriptional and Epigenetic Factors Associated with Early Thrombosis of Femoral Artery Involved in Arteriovenous Fistula

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